{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\Zack.Grant\Nuffield College Dropbox\Zack Grant\ECONOMIC INSECURITY\AI Risks Paper\Journal Submission\Research and Politics\Replication\AIpaperreplication_log.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}18 Nov 2024, 14:44:15

{com}. do "C:\Users\ZACK~1.GRA\AppData\Local\Temp\STD54e0_000000.tmp"
{txt}
{com}. 
. 
. *** Edit following command to reflect the relevant location of our dataset
. use "C:\Users\Zack.Grant\Nuffield College Dropbox\Zack Grant\ECONOMIC INSECURITY\AI Risks Paper\October Empirics\AIpaper_Data_R&Pclean.dta", clear
{txt}
{com}. 
. * Weight Data *
. svyset ES_id [pweight=w8]

      {txt}pweight:{col 16}{res}w8
          {txt}VCE:{col 16}{res}linearized
  {txt}Single unit:{col 16}{res}missing
     {txt}Strata 1:{col 16}<one>
         SU 1:{col 16}{res}ES_id
        {txt}FPC 1:{col 16}<zero>
{p2colreset}{...}

{com}. 
. ******************************************* MAIN ARTICLE ***************************************************
. 
. ************************************************************************************************************
. 
. * FIGURE 1: Beliefs about How Different Economic-Political Trends Might Impact One's Own Job Prospects *
.         
. svy: tab longjobimpact8_recession //* Britain entering an economic recession
{txt}(running {bf:tabulate} on estimation sample)
{res}
{txt}{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     4,249
{txt}{col 1}Number of PSUs{col 20}= {res}    4,249{txt}{col 49}Population size{col 67}={res} 4,434.5119
{txt}{col 49}Design df{col 67}= {res}     4,248

{txt}{hline 10}{c TT}{hline 11}
Job       {c |}
Impact:   {c |}
UK        {c |}
Recession {c |}
(6 Cat    {c |}
Version)  {c |} proportion
{hline 10}{c +}{hline 11}
 Worsen m {c |}      {res}.1239
 {txt}Worsen m {c |}      {res}.2974
  {txt}Have no {c |}      {res}.3542
  {txt}Improve {c |}      {res}.0353
  {txt}Improve {c |}      {res}.0084
 {txt}Don't kn {c |}      {res}.1808
          {txt}{c |} 
    Total {c |}          {res}1
{txt}{hline 10}{c BT}{hline 11}
  Key:  {col 1}proportion  =  {res}cell proportion
{txt}
{com}. svy: tab longjobimpact2_AI        //* The use of Artificial Intelligence in my area of work
{txt}(running {bf:tabulate} on estimation sample)
{res}
{txt}{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     4,249
{txt}{col 1}Number of PSUs{col 20}= {res}    4,249{txt}{col 49}Population size{col 67}={res} 4,434.5119
{txt}{col 49}Design df{col 67}= {res}     4,248

{txt}{hline 10}{c TT}{hline 11}
Job       {c |}
Impact:   {c |}
Artificia {c |}
l         {c |}
Intellige {c |}
nce (6    {c |}
Cat       {c |}
Version)  {c |} proportion
{hline 10}{c +}{hline 11}
 Worsen m {c |}       {res}.068
 {txt}Worsen m {c |}       {res}.165
  {txt}Have no {c |}      {res}.4674
  {txt}Improve {c |}      {res}.0802
  {txt}Improve {c |}       {res}.016
 {txt}Don't kn {c |}      {res}.2033
          {txt}{c |} 
    Total {c |}          {res}1
{txt}{hline 10}{c BT}{hline 11}
  Key:  {col 1}proportion  =  {res}cell proportion
{txt}
{com}. svy: tab longjobimpact3_immigration //* Britain allowing higher rates of immigration
{txt}(running {bf:tabulate} on estimation sample)
{res}
{txt}{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     4,249
{txt}{col 1}Number of PSUs{col 20}= {res}    4,249{txt}{col 49}Population size{col 67}={res} 4,434.5119
{txt}{col 49}Design df{col 67}= {res}     4,248

{txt}{hline 10}{c TT}{hline 11}
Job       {c |}
Impact:   {c |}
Immigrati {c |}
on (6 Cat {c |}
Version)  {c |} proportion
{hline 10}{c +}{hline 11}
 Worsen m {c |}        {res}.09
 {txt}Worsen m {c |}      {res}.1048
  {txt}Have no {c |}      {res}.5831
  {txt}Improve {c |}      {res}.0413
  {txt}Improve {c |}      {res}.0135
 {txt}Don't kn {c |}      {res}.1672
          {txt}{c |} 
    Total {c |}          {res}1
{txt}{hline 10}{c BT}{hline 11}
  Key:  {col 1}proportion  =  {res}cell proportion
{txt}
{com}. svy: tab longjobimpact1_technology //* The expansion of technological advances in the workplace
{txt}(running {bf:tabulate} on estimation sample)
{res}
{txt}{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     4,249
{txt}{col 1}Number of PSUs{col 20}= {res}    4,249{txt}{col 49}Population size{col 67}={res} 4,434.5119
{txt}{col 49}Design df{col 67}= {res}     4,248

{txt}{hline 10}{c TT}{hline 11}
Job       {c |}
Impact:   {c |}
Technolog {c |}
ical      {c |}
Advances  {c |}
(6 Cat    {c |}
Version)  {c |} proportion
{hline 10}{c +}{hline 11}
 Worsen m {c |}      {res}.0415
 {txt}Worsen m {c |}      {res}.1307
  {txt}Have no {c |}      {res}.4959
  {txt}Improve {c |}       {res}.127
  {txt}Improve {c |}      {res}.0312
 {txt}Don't kn {c |}      {res}.1738
          {txt}{c |} 
    Total {c |}          {res}1
{txt}{hline 10}{c BT}{hline 11}
  Key:  {col 1}proportion  =  {res}cell proportion
{txt}
{com}. svy: tab longjobimpact7_imports //* An increase of cheap imported products in the UK
{txt}(running {bf:tabulate} on estimation sample)
{res}
{txt}{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     4,249
{txt}{col 1}Number of PSUs{col 20}= {res}    4,249{txt}{col 49}Population size{col 67}={res} 4,434.5119
{txt}{col 49}Design df{col 67}= {res}     4,248

{txt}{hline 10}{c TT}{hline 11}
Job       {c |}
Impact:   {c |}
More      {c |}
Cheap     {c |}
Imports   {c |}
(6 Cat    {c |}
Version)  {c |} proportion
{hline 10}{c +}{hline 11}
 Worsen m {c |}      {res}.0358
 {txt}Worsen m {c |}      {res}.0689
  {txt}Have no {c |}      {res}.6598
  {txt}Improve {c |}      {res}.0331
  {txt}Improve {c |}      {res}.0117
 {txt}Don't kn {c |}      {res}.1908
          {txt}{c |} 
    Total {c |}          {res}1
{txt}{hline 10}{c BT}{hline 11}
  Key:  {col 1}proportion  =  {res}cell proportion
{txt}
{com}. svy: tab longjobimpact6_greentax //* Efforts to reduce Britain's carbon emissions and combat climate change.
{txt}(running {bf:tabulate} on estimation sample)
{res}
{txt}{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     4,249
{txt}{col 1}Number of PSUs{col 20}= {res}    4,249{txt}{col 49}Population size{col 67}={res} 4,434.5119
{txt}{col 49}Design df{col 67}= {res}     4,248

{txt}{hline 10}{c TT}{hline 11}
Job       {c |}
Impact:   {c |}
Efforts   {c |}
to Reduce {c |}
Climate   {c |}
Change (6 {c |}
Cat       {c |}
Version)  {c |} proportion
{hline 10}{c +}{hline 11}
 Worsen m {c |}      {res}.0357
 {txt}Worsen m {c |}      {res}.0508
  {txt}Have no {c |}      {res}.6352
  {txt}Improve {c |}      {res}.0576
  {txt}Improve {c |}      {res}.0265
 {txt}Don't kn {c |}      {res}.1942
          {txt}{c |} 
    Total {c |}          {res}1
{txt}{hline 10}{c BT}{hline 11}
  Key:  {col 1}proportion  =  {res}cell proportion
{txt}
{com}. 
. 
. ************************************************************************************************************
. 
. * TABLE 1: Distribution of Subjective Exposure to AI by Objective Occupational Exposure (Raw Data) *
.         
. * Set Controls and Sample Size  
. global controls_occupindices std_RTI_Autor std_offshorable_Blinder2007 
{txt}
{com}. global controls_demographics age ib1.female_dummy i.degree_dummy ib2.employmentstatus5 i.employsector5 i.equivincomeHHquintile_HBAI_imp         
{txt}
{com}.  
. svy: logit AI_worsen aiexposure_felten2021 $controls_demographics  $controls_occupindices, or
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}  15{txt},{res}   3947{txt}){col 67}= {res}      6.83
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2}  1.38664{col 50}{space 2} .0913931{col 61}{space 1}    4.96{col 70}{space 3}0.000{col 78}{space 4} 1.218552{col 91}{space 3} 1.577914
{txt}{space 33}age {c |}{col 38}{res}{space 2} .9931619{col 50}{space 2} .0036225{col 61}{space 1}   -1.88{col 70}{space 3}0.060{col 78}{space 4} .9860851{col 91}{space 3} 1.000289
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.123461{col 50}{space 2} .0981423{col 61}{space 1}    1.33{col 70}{space 3}0.183{col 78}{space 4} .9466223{col 91}{space 3} 1.333335
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.284636{col 50}{space 2} .1159889{col 61}{space 1}    2.77{col 70}{space 3}0.006{col 78}{space 4} 1.076223{col 91}{space 3} 1.533409
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} .7809298{col 50}{space 2} .0821598{col 61}{space 1}   -2.35{col 70}{space 3}0.019{col 78}{space 4} .6353774{col 91}{space 3} .9598255
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.041029{col 50}{space 2} .1012722{col 61}{space 1}    0.41{col 70}{space 3}0.679{col 78}{space 4} .8602643{col 91}{space 3} 1.259777
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9613448{col 50}{space 2} .1716262{col 61}{space 1}   -0.22{col 70}{space 3}0.825{col 78}{space 4} .6774388{col 91}{space 3} 1.364232
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.097393{col 50}{space 2} .1593472{col 61}{space 1}    0.64{col 70}{space 3}0.522{col 78}{space 4} .8255157{col 91}{space 3}  1.45881
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8541451{col 50}{space 2} .2433443{col 61}{space 1}   -0.55{col 70}{space 3}0.580{col 78}{space 4} .4885983{col 91}{space 3} 1.493177
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.113198{col 50}{space 2} .1854265{col 61}{space 1}    0.64{col 70}{space 3}0.520{col 78}{space 4} .8030516{col 91}{space 3} 1.543125
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.026798{col 50}{space 2} .1661183{col 61}{space 1}    0.16{col 70}{space 3}0.870{col 78}{space 4} .7477095{col 91}{space 3} 1.410057
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.033278{col 50}{space 2} .1629919{col 61}{space 1}    0.21{col 70}{space 3}0.836{col 78}{space 4} .7584124{col 91}{space 3} 1.407761
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.058673{col 50}{space 2}  .169062{col 61}{space 1}    0.36{col 70}{space 3}0.721{col 78}{space 4} .7740875{col 91}{space 3} 1.447884
{txt}{space 36} {c |}
{space 23}std_RTI_Autor {c |}{col 38}{res}{space 2} 1.454632{col 50}{space 2} .1212435{col 61}{space 1}    4.50{col 70}{space 3}0.000{col 78}{space 4} 1.235332{col 91}{space 3} 1.712862
{txt}{space 9}std_offshorable_Blinder2007 {c |}{col 38}{res}{space 2} 1.088253{col 50}{space 2} .0538328{col 61}{space 1}    1.71{col 70}{space 3}0.087{col 78}{space 4} .9876673{col 91}{space 3} 1.199083
{txt}{space 31}_cons {c |}{col 38}{res}{space 2} .3319043{col 50}{space 2} .0737426{col 61}{space 1}   -4.96{col 70}{space 3}0.000{col 78}{space 4} .2147014{col 91}{space 3} .5130868
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. generate infullmodel_Table1 = e(sample) 
{txt}
{com}.          
.          
. ******* TABLE 1a ******
. 
. ** Descriptives
. svy: tab aiexposure_groupFelten2021 jobimpact2_AI, row stubw(40)
{txt}(running {bf:tabulate} on estimation sample)
{res}
{txt}{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     4,078
{txt}{col 1}Number of PSUs{col 20}= {res}    4,078{txt}{col 49}Population size{col 67}={res}  4,218.972
{txt}{col 49}Design df{col 67}= {res}     4,077

{txt}{hline 33}{c TT}{hline 49}
Exposure to AI: Std. Dev Groups  {c |}    Job Impact: Artificial Intelligence (4 Cat   
Based on Felten et al (2021)-    {c |}                     Version)                    
For descriptives                 {c |} No Impac    Worsen   Improve  Don't Kn     Total
{hline 33}{c +}{hline 49}
Least Exposed (>1 SD Below Mean) {c |}    {res}.6217     .1662     .0326     .1795         1
{txt}Less Exposed (0-1 SD Below Mean) {c |}    {res}.5789     .1491     .0501      .222         1
{txt}More Exposed (0-1 SD Above Mean) {c |}     {res}.475     .2245     .0925     .2079         1
 {txt}Most Exposed (>1 SD Above Mean) {c |}    {res}.4151     .2824     .1261     .1764         1
                                 {txt}{c |} 
                           Total {c |}    {res}.4777     .2337     .0956     .1931         1
{txt}{hline 33}{c BT}{hline 49}
  Key:  {col 1}{res}row proportion

{txt}  Pearson:
{col 5}Uncorrected{col 19}chi2({res}9{txt}){col 35}= {res} 146.7824
{txt}{col 5}Design-based{col 19}F({res}8.93{txt}, {res}36411.06{txt}){col 35}= {res}  12.5201{col 51}{txt}P = {res}0.0000
{txt}
{com}. 
. ** Model 1a
. svy: logit AI_worsen aiexposure_felten2021 if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}   1{txt},{res}   3961{txt}){col 67}= {res}     42.23
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}  Linearized
{col 1}            AI_worsen{col 23}{c |} Odds Ratio{col 35}   Std. Err.{col 47}      t{col 55}   P>|t|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
aiexposure_felten2021 {c |}{col 23}{res}{space 2} 1.418414{col 35}{space 2} .0762961{col 46}{space 1}    6.50{col 55}{space 3}0.000{col 63}{space 4} 1.276448{col 76}{space 3} 1.576169
{txt}{space 16}_cons {c |}{col 23}{res}{space 2} .2546991{col 35}{space 2} .0140968{col 46}{space 1}  -24.71{col 55}{space 3}0.000{col 63}{space 4}  .228508{col 76}{space 3} .2838921
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 1b
. svy: logit AI_worsen aiexposure_felten2021 $controls_demographics if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}  13{txt},{res}   3949{txt}){col 67}= {res}      4.83
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2}  1.41093{col 50}{space 2} .0823244{col 61}{space 1}    5.90{col 70}{space 3}0.000{col 78}{space 4} 1.258417{col 91}{space 3} 1.581926
{txt}{space 33}age {c |}{col 38}{res}{space 2} .9928402{col 50}{space 2} .0035926{col 61}{space 1}   -1.99{col 70}{space 3}0.047{col 78}{space 4} .9858215{col 91}{space 3} .9999088
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.134889{col 50}{space 2} .0979687{col 61}{space 1}    1.47{col 70}{space 3}0.143{col 78}{space 4} .9581894{col 91}{space 3} 1.344174
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.214095{col 50}{space 2} .1085179{col 61}{space 1}    2.17{col 70}{space 3}0.030{col 78}{space 4} 1.018937{col 91}{space 3} 1.446631
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2}  .763106{col 50}{space 2} .0799534{col 61}{space 1}   -2.58{col 70}{space 3}0.010{col 78}{space 4} .6214041{col 91}{space 3} .9371209
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9218484{col 50}{space 2} .0871412{col 61}{space 1}   -0.86{col 70}{space 3}0.389{col 78}{space 4} .7658997{col 91}{space 3} 1.109551
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .8932466{col 50}{space 2} .1594037{col 61}{space 1}   -0.63{col 70}{space 3}0.527{col 78}{space 4} .6295415{col 91}{space 3} 1.267414
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2}  .971413{col 50}{space 2} .1406076{col 61}{space 1}   -0.20{col 70}{space 3}0.841{col 78}{space 4} .7314064{col 91}{space 3} 1.290176
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8249163{col 50}{space 2} .2318424{col 61}{space 1}   -0.68{col 70}{space 3}0.493{col 78}{space 4} .4754522{col 91}{space 3} 1.431241
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.129994{col 50}{space 2} .1875537{col 61}{space 1}    0.74{col 70}{space 3}0.462{col 78}{space 4}  .816117{col 91}{space 3} 1.564587
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.008098{col 50}{space 2} .1622075{col 61}{space 1}    0.05{col 70}{space 3}0.960{col 78}{space 4} .7353577{col 91}{space 3} 1.381996
{txt}{space 34}4  {c |}{col 38}{res}{space 2}  1.00438{col 50}{space 2} .1570008{col 61}{space 1}    0.03{col 70}{space 3}0.978{col 78}{space 4} .7392659{col 91}{space 3} 1.364568
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .9993104{col 50}{space 2} .1575852{col 61}{space 1}   -0.00{col 70}{space 3}0.997{col 78}{space 4} .7335505{col 91}{space 3} 1.361353
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2}  .373499{col 50}{space 2}  .082692{col 61}{space 1}   -4.45{col 70}{space 3}0.000{col 78}{space 4} .2419788{col 91}{space 3}  .576503
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 1c
. svy: logit AI_worsen aiexposure_felten2021 $controls_demographics $controls_occupindices if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}  15{txt},{res}   3947{txt}){col 67}= {res}      6.83
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2}  1.38664{col 50}{space 2} .0913931{col 61}{space 1}    4.96{col 70}{space 3}0.000{col 78}{space 4} 1.218552{col 91}{space 3} 1.577914
{txt}{space 33}age {c |}{col 38}{res}{space 2} .9931619{col 50}{space 2} .0036225{col 61}{space 1}   -1.88{col 70}{space 3}0.060{col 78}{space 4} .9860851{col 91}{space 3} 1.000289
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.123461{col 50}{space 2} .0981423{col 61}{space 1}    1.33{col 70}{space 3}0.183{col 78}{space 4} .9466223{col 91}{space 3} 1.333335
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.284636{col 50}{space 2} .1159889{col 61}{space 1}    2.77{col 70}{space 3}0.006{col 78}{space 4} 1.076223{col 91}{space 3} 1.533409
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} .7809298{col 50}{space 2} .0821598{col 61}{space 1}   -2.35{col 70}{space 3}0.019{col 78}{space 4} .6353774{col 91}{space 3} .9598255
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.041029{col 50}{space 2} .1012722{col 61}{space 1}    0.41{col 70}{space 3}0.679{col 78}{space 4} .8602643{col 91}{space 3} 1.259777
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9613448{col 50}{space 2} .1716262{col 61}{space 1}   -0.22{col 70}{space 3}0.825{col 78}{space 4} .6774388{col 91}{space 3} 1.364232
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.097393{col 50}{space 2} .1593472{col 61}{space 1}    0.64{col 70}{space 3}0.522{col 78}{space 4} .8255157{col 91}{space 3}  1.45881
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8541451{col 50}{space 2} .2433443{col 61}{space 1}   -0.55{col 70}{space 3}0.580{col 78}{space 4} .4885983{col 91}{space 3} 1.493177
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.113198{col 50}{space 2} .1854265{col 61}{space 1}    0.64{col 70}{space 3}0.520{col 78}{space 4} .8030516{col 91}{space 3} 1.543125
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.026798{col 50}{space 2} .1661183{col 61}{space 1}    0.16{col 70}{space 3}0.870{col 78}{space 4} .7477095{col 91}{space 3} 1.410057
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.033278{col 50}{space 2} .1629919{col 61}{space 1}    0.21{col 70}{space 3}0.836{col 78}{space 4} .7584124{col 91}{space 3} 1.407761
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.058673{col 50}{space 2}  .169062{col 61}{space 1}    0.36{col 70}{space 3}0.721{col 78}{space 4} .7740875{col 91}{space 3} 1.447884
{txt}{space 36} {c |}
{space 23}std_RTI_Autor {c |}{col 38}{res}{space 2} 1.454632{col 50}{space 2} .1212435{col 61}{space 1}    4.50{col 70}{space 3}0.000{col 78}{space 4} 1.235332{col 91}{space 3} 1.712862
{txt}{space 9}std_offshorable_Blinder2007 {c |}{col 38}{res}{space 2} 1.088253{col 50}{space 2} .0538328{col 61}{space 1}    1.71{col 70}{space 3}0.087{col 78}{space 4} .9876673{col 91}{space 3} 1.199083
{txt}{space 31}_cons {c |}{col 38}{res}{space 2} .3319043{col 50}{space 2} .0737426{col 61}{space 1}   -4.96{col 70}{space 3}0.000{col 78}{space 4} .2147014{col 91}{space 3} .5130868
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. ** Model 2a
. svy: logit AI_improve aiexposure_felten2021 if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}   1{txt},{res}   3961{txt}){col 67}= {res}     37.96
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}  Linearized
{col 1}           AI_improve{col 23}{c |} Odds Ratio{col 35}   Std. Err.{col 47}      t{col 55}   P>|t|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
aiexposure_felten2021 {c |}{col 23}{res}{space 2} 1.842481{col 35}{space 2} .1827491{col 46}{space 1}    6.16{col 55}{space 3}0.000{col 63}{space 4} 1.516874{col 76}{space 3} 2.237981
{txt}{space 16}_cons {c |}{col 23}{res}{space 2} .0691515{col 35}{space 2} .0072462{col 46}{space 1}  -25.49{col 55}{space 3}0.000{col 63}{space 4} .0563091{col 76}{space 3} .0849228
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 2b
. svy: logit AI_improve aiexposure_felten2021 $controls_demographics if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}  13{txt},{res}   3949{txt}){col 67}= {res}     12.87
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                          AI_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.566415{col 50}{space 2} .1600653{col 61}{space 1}    4.39{col 70}{space 3}0.000{col 78}{space 4} 1.282035{col 91}{space 3} 1.913878
{txt}{space 33}age {c |}{col 38}{res}{space 2} .9693991{col 50}{space 2} .0055578{col 61}{space 1}   -5.42{col 70}{space 3}0.000{col 78}{space 4} .9585636{col 91}{space 3}  .980357
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.717065{col 50}{space 2} .2192722{col 61}{space 1}    4.23{col 70}{space 3}0.000{col 78}{space 4} 1.336761{col 91}{space 3} 2.205565
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  1.75945{col 50}{space 2} .2423788{col 61}{space 1}    4.10{col 70}{space 3}0.000{col 78}{space 4} 1.343015{col 91}{space 3}  2.30501
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} 2.128802{col 50}{space 2} .4370058{col 61}{space 1}    3.68{col 70}{space 3}0.000{col 78}{space 4} 1.423459{col 91}{space 3} 3.183651
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .7492511{col 50}{space 2}  .107168{col 61}{space 1}   -2.02{col 70}{space 3}0.044{col 78}{space 4} .5660304{col 91}{space 3} .9917792
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .7118171{col 50}{space 2} .1990685{col 61}{space 1}   -1.22{col 70}{space 3}0.224{col 78}{space 4} .4113832{col 91}{space 3} 1.231658
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.246509{col 50}{space 2} .2828993{col 61}{space 1}    0.97{col 70}{space 3}0.332{col 78}{space 4} .7988279{col 91}{space 3}  1.94508
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.206108{col 50}{space 2} .6107226{col 61}{space 1}    0.37{col 70}{space 3}0.711{col 78}{space 4} .4469319{col 91}{space 3} 3.254849
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5007042{col 50}{space 2} .1490934{col 61}{space 1}   -2.32{col 70}{space 3}0.020{col 78}{space 4} .2792825{col 91}{space 3} .8976741
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .4284354{col 50}{space 2} .1165606{col 61}{space 1}   -3.12{col 70}{space 3}0.002{col 78}{space 4} .2513251{col 91}{space 3} .7303565
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .5153574{col 50}{space 2} .1254572{col 61}{space 1}   -2.72{col 70}{space 3}0.006{col 78}{space 4} .3197651{col 91}{space 3} .8305889
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .6979932{col 50}{space 2} .1635321{col 61}{space 1}   -1.53{col 70}{space 3}0.125{col 78}{space 4} .4409234{col 91}{space 3} 1.104942
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .1467656{col 50}{space 2} .0550539{col 61}{space 1}   -5.12{col 70}{space 3}0.000{col 78}{space 4} .0703442{col 91}{space 3} .3062105
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 2c
. svy: logit AI_improve aiexposure_felten2021 $controls_demographics $controls_occupindices if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}  15{txt},{res}   3947{txt}){col 67}= {res}     11.15
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                          AI_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.468642{col 50}{space 2} .1685801{col 61}{space 1}    3.35{col 70}{space 3}0.001{col 78}{space 4}  1.17268{col 91}{space 3} 1.839298
{txt}{space 33}age {c |}{col 38}{res}{space 2} .9691886{col 50}{space 2} .0055792{col 61}{space 1}   -5.44{col 70}{space 3}0.000{col 78}{space 4} .9583117{col 91}{space 3}  .980189
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.693684{col 50}{space 2} .2168789{col 61}{space 1}    4.11{col 70}{space 3}0.000{col 78}{space 4} 1.317654{col 91}{space 3} 2.177025
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.725848{col 50}{space 2} .2409989{col 61}{space 1}    3.91{col 70}{space 3}0.000{col 78}{space 4} 1.312513{col 91}{space 3} 2.269349
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} 2.074592{col 50}{space 2} .4252539{col 61}{space 1}    3.56{col 70}{space 3}0.000{col 78}{space 4} 1.388029{col 91}{space 3} 3.100752
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .7535466{col 50}{space 2}  .113038{col 61}{space 1}   -1.89{col 70}{space 3}0.059{col 78}{space 4} .5615443{col 91}{space 3} 1.011198
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .7131232{col 50}{space 2} .2009502{col 61}{space 1}   -1.20{col 70}{space 3}0.230{col 78}{space 4} .4104234{col 91}{space 3} 1.239073
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.201297{col 50}{space 2} .2748975{col 61}{space 1}    0.80{col 70}{space 3}0.423{col 78}{space 4} .7670205{col 91}{space 3} 1.881454
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.226089{col 50}{space 2} .6238686{col 61}{space 1}    0.40{col 70}{space 3}0.689{col 78}{space 4} .4521417{col 91}{space 3} 3.324832
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5010162{col 50}{space 2}  .148981{col 61}{space 1}   -2.32{col 70}{space 3}0.020{col 78}{space 4} .2796811{col 91}{space 3} .8975123
{txt}{space 34}3  {c |}{col 38}{res}{space 2}  .426497{col 50}{space 2}  .115992{col 61}{space 1}   -3.13{col 70}{space 3}0.002{col 78}{space 4} .2502353{col 91}{space 3} .7269145
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .5101341{col 50}{space 2} .1247094{col 61}{space 1}   -2.75{col 70}{space 3}0.006{col 78}{space 4} .3158877{col 91}{space 3}  .823827
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .6756307{col 50}{space 2} .1586321{col 61}{space 1}   -1.67{col 70}{space 3}0.095{col 78}{space 4}  .426377{col 91}{space 3} 1.070595
{txt}{space 36} {c |}
{space 23}std_RTI_Autor {c |}{col 38}{res}{space 2} .8142681{col 50}{space 2} .1151217{col 61}{space 1}   -1.45{col 70}{space 3}0.146{col 78}{space 4} .6171451{col 91}{space 3} 1.074354
{txt}{space 9}std_offshorable_Blinder2007 {c |}{col 38}{res}{space 2}   1.0862{col 50}{space 2} .0788164{col 61}{space 1}    1.14{col 70}{space 3}0.255{col 78}{space 4} .9421639{col 91}{space 3} 1.252256
{txt}{space 31}_cons {c |}{col 38}{res}{space 2} .1577769{col 50}{space 2} .0599877{col 61}{space 1}   -4.86{col 70}{space 3}0.000{col 78}{space 4} .0748709{col 91}{space 3} .3324864
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. ** Model 3a
. svy: logit AI_directionworse aiexposure_felten2021 if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}   1{txt},{res}   1321{txt}){col 67}= {res}      7.20
{txt}{col 49}Prob > F{col 67}= {res}    0.0074

{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}  Linearized
{col 1}    AI_directionworse{col 23}{c |} Odds Ratio{col 35}   Std. Err.{col 47}      t{col 55}   P>|t|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
aiexposure_felten2021 {c |}{col 23}{res}{space 2} .7601341{col 35}{space 2} .0776837{col 46}{space 1}   -2.68{col 55}{space 3}0.007{col 63}{space 4} .6220423{col 76}{space 3} .9288821
{txt}{space 16}_cons {c |}{col 23}{res}{space 2} 3.104447{col 35}{space 2} .3424206{col 46}{space 1}   10.27{col 55}{space 3}0.000{col 63}{space 4} 2.500406{col 76}{space 3}  3.85441
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 3b
. svy: logit AI_directionworse aiexposure_felten2021 $controls_demographics if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}  13{txt},{res}   1309{txt}){col 67}= {res}      5.62
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                   AI_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .8435581{col 50}{space 2} .0899951{col 61}{space 1}   -1.59{col 70}{space 3}0.111{col 78}{space 4}   .68426{col 91}{space 3} 1.039941
{txt}{space 33}age {c |}{col 38}{res}{space 2} 1.021888{col 50}{space 2} .0063162{col 61}{space 1}    3.50{col 70}{space 3}0.000{col 78}{space 4} 1.009572{col 91}{space 3} 1.034354
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} .6928938{col 50}{space 2} .0993854{col 61}{space 1}   -2.56{col 70}{space 3}0.011{col 78}{space 4} .5229527{col 91}{space 3} .9180598
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  .698706{col 50}{space 2} .1064464{col 61}{space 1}   -2.35{col 70}{space 3}0.019{col 78}{space 4} .5181993{col 91}{space 3} .9420896
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} .4152781{col 50}{space 2} .0931261{col 61}{space 1}   -3.92{col 70}{space 3}0.000{col 78}{space 4} .2674743{col 91}{space 3} .6447569
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.215059{col 50}{space 2} .1958642{col 61}{space 1}    1.21{col 70}{space 3}0.227{col 78}{space 4} .8856464{col 91}{space 3} 1.666996
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.260586{col 50}{space 2} .4078532{col 61}{space 1}    0.72{col 70}{space 3}0.474{col 78}{space 4} .6682209{col 91}{space 3} 2.378072
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9058492{col 50}{space 2} .2407181{col 61}{space 1}   -0.37{col 70}{space 3}0.710{col 78}{space 4}  .537839{col 91}{space 3} 1.525666
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .7311951{col 50}{space 2} .3968104{col 61}{space 1}   -0.58{col 70}{space 3}0.564{col 78}{space 4} .2521583{col 91}{space 3}  2.12028
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}  1.92555{col 50}{space 2}  .654354{col 61}{space 1}    1.93{col 70}{space 3}0.054{col 78}{space 4} .9886171{col 91}{space 3} 3.750433
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 2.183719{col 50}{space 2} .6699048{col 61}{space 1}    2.55{col 70}{space 3}0.011{col 78}{space 4} 1.196278{col 91}{space 3} 3.986221
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.748867{col 50}{space 2} .4897441{col 61}{space 1}    2.00{col 70}{space 3}0.046{col 78}{space 4} 1.009652{col 91}{space 3} 3.029296
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.319614{col 50}{space 2} .3588896{col 61}{space 1}    1.02{col 70}{space 3}0.308{col 78}{space 4} .7739932{col 91}{space 3} 2.249867
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 2.294478{col 50}{space 2}  .895971{col 61}{space 1}    2.13{col 70}{space 3}0.034{col 78}{space 4}  1.06658{col 91}{space 3} 4.935993
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 3c
. svy: logit AI_directionworse aiexposure_felten2021 $controls_demographics $controls_occupindices if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}  15{txt},{res}   1307{txt}){col 67}= {res}      5.13
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                   AI_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .8918794{col 50}{space 2} .1080793{col 61}{space 1}   -0.94{col 70}{space 3}0.345{col 78}{space 4}  .703172{col 91}{space 3} 1.131229
{txt}{space 33}age {c |}{col 38}{res}{space 2} 1.022598{col 50}{space 2} .0064167{col 61}{space 1}    3.56{col 70}{space 3}0.000{col 78}{space 4} 1.010087{col 91}{space 3} 1.035264
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} .7092891{col 50}{space 2}  .102144{col 61}{space 1}   -2.39{col 70}{space 3}0.017{col 78}{space 4} .5347246{col 91}{space 3} .9408413
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7334765{col 50}{space 2} .1137611{col 61}{space 1}   -2.00{col 70}{space 3}0.046{col 78}{space 4} .5410596{col 91}{space 3} .9943226
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} .4406998{col 50}{space 2} .0995899{col 61}{space 1}   -3.63{col 70}{space 3}0.000{col 78}{space 4} .2828856{col 91}{space 3} .6865542
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.299859{col 50}{space 2}  .221364{col 61}{space 1}    1.54{col 70}{space 3}0.124{col 78}{space 4} .9306895{col 91}{space 3} 1.815463
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}  1.25828{col 50}{space 2} .4031593{col 61}{space 1}    0.72{col 70}{space 3}0.473{col 78}{space 4} .6711163{col 91}{space 3} 2.359156
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.005385{col 50}{space 2} .2675771{col 61}{space 1}    0.02{col 70}{space 3}0.984{col 78}{space 4} .5964617{col 91}{space 3} 1.694659
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .7269527{col 50}{space 2} .3970539{col 61}{space 1}   -0.58{col 70}{space 3}0.559{col 78}{space 4} .2489789{col 91}{space 3} 2.122511
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.865272{col 50}{space 2} .6314466{col 61}{space 1}    1.84{col 70}{space 3}0.066{col 78}{space 4} .9601136{col 91}{space 3} 3.623781
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 2.158848{col 50}{space 2} .6611652{col 61}{space 1}    2.51{col 70}{space 3}0.012{col 78}{space 4} 1.183847{col 91}{space 3} 3.936849
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.787434{col 50}{space 2} .5019315{col 61}{space 1}    2.07{col 70}{space 3}0.039{col 78}{space 4} 1.030348{col 91}{space 3} 3.100817
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.380914{col 50}{space 2} .3723912{col 61}{space 1}    1.20{col 70}{space 3}0.232{col 78}{space 4} .8136031{col 91}{space 3} 2.343801
{txt}{space 36} {c |}
{space 23}std_RTI_Autor {c |}{col 38}{res}{space 2} 1.521521{col 50}{space 2} .2546298{col 61}{space 1}    2.51{col 70}{space 3}0.012{col 78}{space 4} 1.095714{col 91}{space 3} 2.112803
{txt}{space 9}std_offshorable_Blinder2007 {c |}{col 38}{res}{space 2}  .976218{col 50}{space 2} .0795256{col 61}{space 1}   -0.30{col 70}{space 3}0.768{col 78}{space 4} .8320355{col 91}{space 3} 1.145386
{txt}{space 31}_cons {c |}{col 38}{res}{space 2} 1.939541{col 50}{space 2} .7832764{col 61}{space 1}    1.64{col 70}{space 3}0.101{col 78}{space 4} .8782726{col 91}{space 3} 4.283204
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. 
. ******* TABLE 1b ******
. 
. ** Descriptives
. svy: tab aiexposure_pizzinelli2023 jobimpact2_AI, row stubw(40)
{txt}(running {bf:tabulate} on estimation sample)
{res}
{txt}{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     4,078
{txt}{col 1}Number of PSUs{col 20}= {res}    4,078{txt}{col 49}Population size{col 67}={res}  4,218.972
{txt}{col 49}Design df{col 67}= {res}     4,077

{txt}{hline 33}{c TT}{hline 49}
Exposure to AI: Simplified       {c |}    Job Impact: Artificial Intelligence (4 Cat   
Groups Based on Pizzinelli et al {c |}                     Version)                    
(2023)                           {c |} No Impac    Worsen   Improve  Don't Kn     Total
{hline 33}{c +}{hline 49}
                    Low Exposure {c |}    {res}.5976     .1515     .0438     .2072         1
   {txt}High Exposure, Low Compliment {c |}    {res}.4206     .2857     .1039     .1899         1
  {txt}High Exposure, High Compliment {c |}    {res}.4706     .2176     .1252     .1866         1
                                 {txt}{c |} 
                           Total {c |}    {res}.4777     .2337     .0956     .1931         1
{txt}{hline 33}{c BT}{hline 49}
  Key:  {col 1}{res}row proportion

{txt}  Pearson:
{col 5}Uncorrected{col 19}chi2({res}6{txt}){col 35}= {res} 135.7454
{txt}{col 5}Design-based{col 19}F({res}5.97{txt}, {res}24329.02{txt}){col 35}= {res}  18.3662{col 51}{txt}P = {res}0.0000
{txt}
{com}. 
. ** Model 4a
. svy: logit AI_worsen i.aiexposure_pizzinelli2023 if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}   2{txt},{res}   3960{txt}){col 67}= {res}     26.05
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 33}{c |}{col 45}  Linearized
{col 1}                      AI_worsen{col 33}{c |} Odds Ratio{col 45}   Std. Err.{col 57}      t{col 65}   P>|t|{col 73}     [95% Con{col 86}f. Interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}aiexposure_pizzinelli2023 {c |}
{space 1}High Exposure, Low Compliment  {c |}{col 33}{res}{space 2} 2.253628{col 45}{space 2} .2685664{col 56}{space 1}    6.82{col 65}{space 3}0.000{col 73}{space 4} 1.784075{col 86}{space 3} 2.846765
{txt}High Exposure, High Compliment  {c |}{col 33}{res}{space 2} 1.541842{col 45}{space 2} .2002989{col 56}{space 1}    3.33{col 65}{space 3}0.001{col 73}{space 4} 1.195164{col 86}{space 3} 1.989081
{txt}{space 31} {c |}
{space 26}_cons {c |}{col 33}{res}{space 2} .1812414{col 45}{space 2} .0192322{col 56}{space 1}  -16.10{col 65}{space 3}0.000{col 73}{space 4} .1471994{col 86}{space 3} .2231563
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 4b
. svy: logit AI_worsen i.aiexposure_pizzinelli2023 $controls_demographics if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}  14{txt},{res}   3948{txt}){col 67}= {res}      5.50
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 2.127966{col 50}{space 2} .2662908{col 61}{space 1}    6.03{col 70}{space 3}0.000{col 78}{space 4} 1.664999{col 91}{space 3} 2.719666
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.444545{col 50}{space 2} .2003068{col 61}{space 1}    2.65{col 70}{space 3}0.008{col 78}{space 4} 1.100687{col 91}{space 3} 1.895825
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2} .9933672{col 50}{space 2} .0035854{col 61}{space 1}   -1.84{col 70}{space 3}0.065{col 78}{space 4} .9863625{col 91}{space 3} 1.000422
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.120267{col 50}{space 2} .0973125{col 61}{space 1}    1.31{col 70}{space 3}0.191{col 78}{space 4} .9448416{col 91}{space 3} 1.328263
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.274102{col 50}{space 2} .1143964{col 61}{space 1}    2.70{col 70}{space 3}0.007{col 78}{space 4} 1.068452{col 91}{space 3} 1.519334
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} .8055607{col 50}{space 2} .0841885{col 61}{space 1}   -2.07{col 70}{space 3}0.039{col 78}{space 4} .6563156{col 91}{space 3} .9887439
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9406172{col 50}{space 2} .0894352{col 61}{space 1}   -0.64{col 70}{space 3}0.520{col 78}{space 4} .7806471{col 91}{space 3} 1.133368
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}  .934951{col 50}{space 2} .1678696{col 61}{space 1}   -0.37{col 70}{space 3}0.708{col 78}{space 4} .6575211{col 91}{space 3} 1.329438
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9959776{col 50}{space 2} .1445435{col 61}{space 1}   -0.03{col 70}{space 3}0.978{col 78}{space 4} .7493406{col 91}{space 3} 1.323793
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .7964022{col 50}{space 2} .2207405{col 61}{space 1}   -0.82{col 70}{space 3}0.412{col 78}{space 4} .4625205{col 91}{space 3} 1.371305
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.131899{col 50}{space 2} .1885877{col 61}{space 1}    0.74{col 70}{space 3}0.457{col 78}{space 4} .8164775{col 91}{space 3} 1.569175
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.043966{col 50}{space 2} .1683346{col 61}{space 1}    0.27{col 70}{space 3}0.790{col 78}{space 4} .7610135{col 91}{space 3} 1.432124
{txt}{space 34}4  {c |}{col 38}{res}{space 2}  1.07852{col 50}{space 2} .1697266{col 61}{space 1}    0.48{col 70}{space 3}0.631{col 78}{space 4} .7921978{col 91}{space 3} 1.468326
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.112506{col 50}{space 2} .1751573{col 61}{space 1}    0.68{col 70}{space 3}0.498{col 78}{space 4}  .817043{col 91}{space 3} 1.514816
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2396575{col 50}{space 2} .0578177{col 61}{space 1}   -5.92{col 70}{space 3}0.000{col 78}{space 4} .1493395{col 91}{space 3} .3845984
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 4c
. svy: logit AI_worsen i.aiexposure_pizzinelli2023 $controls_demographics $controls_occupindices if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}  16{txt},{res}   3946{txt}){col 67}= {res}      6.39
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.923251{col 50}{space 2} .2674089{col 61}{space 1}    4.70{col 70}{space 3}0.000{col 78}{space 4} 1.464362{col 91}{space 3} 2.525941
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.705886{col 50}{space 2} .2632752{col 61}{space 1}    3.46{col 70}{space 3}0.001{col 78}{space 4} 1.260494{col 91}{space 3} 2.308655
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2} .9933648{col 50}{space 2} .0036102{col 61}{space 1}   -1.83{col 70}{space 3}0.067{col 78}{space 4} .9863119{col 91}{space 3} 1.000468
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.116024{col 50}{space 2} .0979441{col 61}{space 1}    1.25{col 70}{space 3}0.211{col 78}{space 4} .9396106{col 91}{space 3}  1.32556
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.302961{col 50}{space 2} .1179495{col 61}{space 1}    2.92{col 70}{space 3}0.003{col 78}{space 4} 1.091072{col 91}{space 3} 1.555999
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} .8004222{col 50}{space 2} .0843795{col 61}{space 1}   -2.11{col 70}{space 3}0.035{col 78}{space 4} .6509672{col 91}{space 3} .9841904
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.036596{col 50}{space 2} .1007126{col 61}{space 1}    0.37{col 70}{space 3}0.711{col 78}{space 4} .8568089{col 91}{space 3} 1.254108
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9744011{col 50}{space 2} .1743981{col 61}{space 1}   -0.14{col 70}{space 3}0.885{col 78}{space 4} .6860303{col 91}{space 3} 1.383988
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.099719{col 50}{space 2} .1603478{col 61}{space 1}    0.65{col 70}{space 3}0.514{col 78}{space 4} .8262885{col 91}{space 3} 1.463631
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8298116{col 50}{space 2} .2328845{col 61}{space 1}   -0.66{col 70}{space 3}0.506{col 78}{space 4}  .478651{col 91}{space 3}   1.4386
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}  1.11893{col 50}{space 2} .1866425{col 61}{space 1}    0.67{col 70}{space 3}0.501{col 78}{space 4} .8068175{col 91}{space 3} 1.551781
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.049268{col 50}{space 2}   .16988{col 61}{space 1}    0.30{col 70}{space 3}0.766{col 78}{space 4} .7638918{col 91}{space 3} 1.441255
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.075045{col 50}{space 2} .1703975{col 61}{space 1}    0.46{col 70}{space 3}0.648{col 78}{space 4} .7878938{col 91}{space 3} 1.466849
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.118386{col 50}{space 2}  .178308{col 61}{space 1}    0.70{col 70}{space 3}0.483{col 78}{space 4} .8181642{col 91}{space 3} 1.528774
{txt}{space 36} {c |}
{space 23}std_RTI_Autor {c |}{col 38}{res}{space 2} 1.386005{col 50}{space 2} .1245028{col 61}{space 1}    3.63{col 70}{space 3}0.000{col 78}{space 4} 1.162195{col 91}{space 3} 1.652914
{txt}{space 9}std_offshorable_Blinder2007 {c |}{col 38}{res}{space 2}  1.11532{col 50}{space 2} .0534567{col 61}{space 1}    2.28{col 70}{space 3}0.023{col 78}{space 4} 1.015288{col 91}{space 3} 1.225207
{txt}{space 31}_cons {c |}{col 38}{res}{space 2} .2299905{col 50}{space 2} .0563192{col 61}{space 1}   -6.00{col 70}{space 3}0.000{col 78}{space 4} .1423007{col 91}{space 3} .3717172
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. ** Model 5a
. svy: logit AI_improve i.aiexposure_pizzinelli2023 if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}   2{txt},{res}   3960{txt}){col 67}= {res}     15.52
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 33}{c |}{col 45}  Linearized
{col 1}                     AI_improve{col 33}{c |} Odds Ratio{col 45}   Std. Err.{col 57}      t{col 65}   P>|t|{col 73}     [95% Con{col 86}f. Interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}aiexposure_pizzinelli2023 {c |}
{space 1}High Exposure, Low Compliment  {c |}{col 33}{res}{space 2} 2.531605{col 45}{space 2} .5272212{col 56}{space 1}    4.46{col 65}{space 3}0.000{col 73}{space 4} 1.682961{col 86}{space 3} 3.808182
{txt}High Exposure, High Compliment  {c |}{col 33}{res}{space 2}  3.25763{col 45}{space 2} .6905999{col 56}{space 1}    5.57{col 65}{space 3}0.000{col 73}{space 4} 2.149792{col 86}{space 3} 4.936363
{txt}{space 31} {c |}
{space 26}_cons {c |}{col 33}{res}{space 2}  .044145{col 45}{space 2} .0083744{col 56}{space 1}  -16.45{col 65}{space 3}0.000{col 73}{space 4} .0304339{col 86}{space 3} .0640332
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 5b
. svy: logit AI_improve i.aiexposure_pizzinelli2023 $controls_demographics if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}  14{txt},{res}   3948{txt}){col 67}= {res}     11.85
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                          AI_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 2.035855{col 50}{space 2} .4522144{col 61}{space 1}    3.20{col 70}{space 3}0.001{col 78}{space 4} 1.317091{col 91}{space 3} 3.146863
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2}  2.55617{col 50}{space 2} .5905945{col 61}{space 1}    4.06{col 70}{space 3}0.000{col 78}{space 4} 1.625033{col 91}{space 3} 4.020843
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2} .9689069{col 50}{space 2} .0055329{col 61}{space 1}   -5.53{col 70}{space 3}0.000{col 78}{space 4} .9581199{col 91}{space 3} .9798154
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.752125{col 50}{space 2} .2255391{col 61}{space 1}    4.36{col 70}{space 3}0.000{col 78}{space 4} 1.361327{col 91}{space 3} 2.255112
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.793201{col 50}{space 2} .2481294{col 61}{space 1}    4.22{col 70}{space 3}0.000{col 78}{space 4} 1.367131{col 91}{space 3} 2.352056
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} 2.172154{col 50}{space 2} .4432291{col 61}{space 1}    3.80{col 70}{space 3}0.000{col 78}{space 4}  1.45596{col 91}{space 3} 3.240648
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2}   .70643{col 50}{space 2}  .102319{col 61}{space 1}   -2.40{col 70}{space 3}0.016{col 78}{space 4} .5317944{col 91}{space 3} .9384141
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .6770116{col 50}{space 2}  .190249{col 61}{space 1}   -1.39{col 70}{space 3}0.165{col 78}{space 4} .3902334{col 91}{space 3}  1.17454
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2}  1.23869{col 50}{space 2} .2810971{col 61}{space 1}    0.94{col 70}{space 3}0.346{col 78}{space 4} .7938524{col 91}{space 3} 1.932794
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.100193{col 50}{space 2} .5629756{col 61}{space 1}    0.19{col 70}{space 3}0.852{col 78}{space 4} .4034321{col 91}{space 3} 3.000317
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5154177{col 50}{space 2} .1530353{col 61}{space 1}   -2.23{col 70}{space 3}0.026{col 78}{space 4} .2879701{col 91}{space 3} .9225103
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .4436094{col 50}{space 2}  .121602{col 61}{space 1}   -2.97{col 70}{space 3}0.003{col 78}{space 4} .2591783{col 91}{space 3} .7592817
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .5415207{col 50}{space 2} .1313897{col 61}{space 1}   -2.53{col 70}{space 3}0.012{col 78}{space 4} .3365302{col 91}{space 3} .8713768
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .7509072{col 50}{space 2} .1760488{col 61}{space 1}   -1.22{col 70}{space 3}0.222{col 78}{space 4} .4742012{col 91}{space 3} 1.189077
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .0973069{col 50}{space 2} .0394148{col 61}{space 1}   -5.75{col 70}{space 3}0.000{col 78}{space 4} .0439798{col 91}{space 3} .2152947
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 5c
. svy: logit AI_improve i.aiexposure_pizzinelli2023 $controls_demographics $controls_occupindices if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}  16{txt},{res}   3946{txt}){col 67}= {res}     10.68
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                          AI_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.675884{col 50}{space 2} .4164535{col 61}{space 1}    2.08{col 70}{space 3}0.038{col 78}{space 4} 1.029574{col 91}{space 3} 2.727911
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 2.191788{col 50}{space 2} .5537442{col 61}{space 1}    3.11{col 70}{space 3}0.002{col 78}{space 4} 1.335614{col 91}{space 3} 3.596798
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2}  .968715{col 50}{space 2} .0055566{col 61}{space 1}   -5.54{col 70}{space 3}0.000{col 78}{space 4} .9578819{col 91}{space 3} .9796705
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.699053{col 50}{space 2} .2197333{col 61}{space 1}    4.10{col 70}{space 3}0.000{col 78}{space 4}  1.31853{col 91}{space 3} 2.189394
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.749331{col 50}{space 2} .2450256{col 61}{space 1}    3.99{col 70}{space 3}0.000{col 78}{space 4} 1.329258{col 91}{space 3} 2.302156
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} 2.078264{col 50}{space 2} .4251374{col 61}{space 1}    3.58{col 70}{space 3}0.000{col 78}{space 4} 1.391626{col 91}{space 3} 3.103694
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .7511533{col 50}{space 2} .1144864{col 61}{space 1}   -1.88{col 70}{space 3}0.061{col 78}{space 4} .5571264{col 91}{space 3} 1.012753
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .6970752{col 50}{space 2}  .198283{col 61}{space 1}   -1.27{col 70}{space 3}0.205{col 78}{space 4} .3990997{col 91}{space 3} 1.217525
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.227127{col 50}{space 2} .2791133{col 61}{space 1}    0.90{col 70}{space 3}0.368{col 78}{space 4} .7856379{col 91}{space 3} 1.916712
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.163564{col 50}{space 2} .5956368{col 61}{space 1}    0.30{col 70}{space 3}0.767{col 78}{space 4} .4265017{col 91}{space 3} 3.174385
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5087973{col 50}{space 2} .1514218{col 61}{space 1}   -2.27{col 70}{space 3}0.023{col 78}{space 4} .2838858{col 91}{space 3} .9118975
{txt}{space 34}3  {c |}{col 38}{res}{space 2}  .438489{col 50}{space 2} .1205689{col 61}{space 1}   -3.00{col 70}{space 3}0.003{col 78}{space 4} .2557625{col 91}{space 3} .7517622
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .5279368{col 50}{space 2} .1288841{col 61}{space 1}   -2.62{col 70}{space 3}0.009{col 78}{space 4}  .327127{col 91}{space 3} .8520157
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .7110663{col 50}{space 2} .1668514{col 61}{space 1}   -1.45{col 70}{space 3}0.146{col 78}{space 4} .4488643{col 91}{space 3} 1.126433
{txt}{space 36} {c |}
{space 23}std_RTI_Autor {c |}{col 38}{res}{space 2} .8524696{col 50}{space 2}  .135536{col 61}{space 1}   -1.00{col 70}{space 3}0.315{col 78}{space 4} .6241703{col 91}{space 3} 1.164273
{txt}{space 9}std_offshorable_Blinder2007 {c |}{col 38}{res}{space 2} 1.168567{col 50}{space 2} .0850367{col 61}{space 1}    2.14{col 70}{space 3}0.032{col 78}{space 4} 1.013194{col 91}{space 3} 1.347766
{txt}{space 31}_cons {c |}{col 38}{res}{space 2} .1177988{col 50}{space 2} .0494146{col 61}{space 1}   -5.10{col 70}{space 3}0.000{col 78}{space 4} .0517568{col 91}{space 3} .2681108
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. ** Model 6a
. svy: logit AI_directionworse ib3.aiexposure_pizzinelli2023 if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}   2{txt},{res}   1320{txt}){col 67}= {res}      8.12
{txt}{col 49}Prob > F{col 67}= {res}    0.0003

{txt}{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 32}{c |}{col 44}  Linearized
{col 1}             AI_directionworse{col 32}{c |} Odds Ratio{col 44}   Std. Err.{col 56}      t{col 64}   P>|t|{col 72}     [95% Con{col 85}f. Interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}aiexposure_pizzinelli2023 {c |}
{space 17}Low Exposure  {c |}{col 32}{res}{space 2} 2.089068{col 44}{space 2}  .495564{col 55}{space 1}    3.11{col 64}{space 3}0.002{col 72}{space 4} 1.311739{col 85}{space 3} 3.327039
{txt}High Exposure, Low Compliment  {c |}{col 32}{res}{space 2} 1.660678{col 44}{space 2} .2404057{col 55}{space 1}    3.50{col 64}{space 3}0.000{col 72}{space 4} 1.250113{col 85}{space 3} 2.206081
{txt}{space 30} {c |}
{space 25}_cons {c |}{col 32}{res}{space 2} 1.737184{col 44}{space 2} .1916768{col 55}{space 1}    5.01{col 64}{space 3}0.000{col 72}{space 4} 1.399072{col 85}{space 3} 2.157007
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 6b
. svy: logit AI_directionworse ib3.aiexposure_pizzinelli2023 $controls_demographics if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}  14{txt},{res}   1308{txt}){col 67}= {res}      5.60
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                   AI_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 23}Low Exposure  {c |}{col 38}{res}{space 2} 1.767858{col 50}{space 2} .4504094{col 61}{space 1}    2.24{col 70}{space 3}0.025{col 78}{space 4} 1.072462{col 91}{space 3} 2.914156
{txt}{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.514567{col 50}{space 2} .2338704{col 61}{space 1}    2.69{col 70}{space 3}0.007{col 78}{space 4} 1.118743{col 91}{space 3} 2.050437
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2} 1.023025{col 50}{space 2}  .006361{col 61}{space 1}    3.66{col 70}{space 3}0.000{col 78}{space 4} 1.010622{col 91}{space 3}  1.03558
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} .6892241{col 50}{space 2}  .099762{col 61}{space 1}   -2.57{col 70}{space 3}0.010{col 78}{space 4} .5188479{col 91}{space 3} .9155475
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7158633{col 50}{space 2} .1088969{col 61}{space 1}   -2.20{col 70}{space 3}0.028{col 78}{space 4} .5311619{col 91}{space 3} .9647911
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} .4318928{col 50}{space 2} .0970202{col 61}{space 1}   -3.74{col 70}{space 3}0.000{col 78}{space 4} .2779631{col 91}{space 3} .6710653
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2}  1.29274{col 50}{space 2} .2121279{col 61}{space 1}    1.56{col 70}{space 3}0.118{col 78}{space 4} .9369317{col 91}{space 3} 1.783669
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.328013{col 50}{space 2} .4280008{col 61}{space 1}    0.88{col 70}{space 3}0.379{col 78}{space 4} .7056993{col 91}{space 3} 2.499106
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .8962509{col 50}{space 2} .2379814{col 61}{space 1}   -0.41{col 70}{space 3}0.680{col 78}{space 4} .5323569{col 91}{space 3} 1.508886
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .7679292{col 50}{space 2} .4224479{col 61}{space 1}   -0.48{col 70}{space 3}0.631{col 78}{space 4} .2609965{col 91}{space 3} 2.259476
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}  1.84271{col 50}{space 2} .6284769{col 61}{space 1}    1.79{col 70}{space 3}0.073{col 78}{space 4} .9437973{col 91}{space 3} 3.597784
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 2.097407{col 50}{space 2} .6520903{col 61}{space 1}    2.38{col 70}{space 3}0.017{col 78}{space 4} 1.139722{col 91}{space 3} 3.859816
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.763784{col 50}{space 2} .4975159{col 61}{space 1}    2.01{col 70}{space 3}0.044{col 78}{space 4} 1.014202{col 91}{space 3} 3.067373
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.302754{col 50}{space 2}  .357581{col 61}{space 1}    0.96{col 70}{space 3}0.335{col 78}{space 4} .7603432{col 91}{space 3} 2.232108
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 1.357062{col 50}{space 2} .5756629{col 61}{space 1}    0.72{col 70}{space 3}0.472{col 78}{space 4} .5904602{col 91}{space 3} 3.118952
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 6c
. svy: logit AI_directionworse ib3.aiexposure_pizzinelli2023 $controls_demographics $controls_occupindices if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}  16{txt},{res}   1306{txt}){col 67}= {res}      5.00
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                   AI_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 23}Low Exposure  {c |}{col 38}{res}{space 2} 1.403706{col 50}{space 2} .3958549{col 61}{space 1}    1.20{col 70}{space 3}0.229{col 78}{space 4} .8072555{col 91}{space 3}  2.44085
{txt}{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.338442{col 50}{space 2} .2540643{col 61}{space 1}    1.54{col 70}{space 3}0.125{col 78}{space 4} .9223074{col 91}{space 3} 1.942333
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2}  1.02341{col 50}{space 2}  .006428{col 61}{space 1}    3.68{col 70}{space 3}0.000{col 78}{space 4} 1.010878{col 91}{space 3} 1.036099
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} .7109707{col 50}{space 2} .1034902{col 61}{space 1}   -2.34{col 70}{space 3}0.019{col 78}{space 4} .5343621{col 91}{space 3}  .945949
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7382106{col 50}{space 2} .1141304{col 61}{space 1}   -1.96{col 70}{space 3}0.050{col 78}{space 4} .5450801{col 91}{space 3} .9997704
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} .4494468{col 50}{space 2}  .101425{col 61}{space 1}   -3.54{col 70}{space 3}0.000{col 78}{space 4} .2886786{col 91}{space 3} .6997484
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.298117{col 50}{space 2} .2229151{col 61}{space 1}    1.52{col 70}{space 3}0.129{col 78}{space 4} .9268513{col 91}{space 3} 1.818101
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.297801{col 50}{space 2} .4192695{col 61}{space 1}    0.81{col 70}{space 3}0.420{col 78}{space 4} .6885979{col 91}{space 3} 2.445968
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9778898{col 50}{space 2} .2594475{col 61}{space 1}   -0.08{col 70}{space 3}0.933{col 78}{space 4} .5810954{col 91}{space 3} 1.645631
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .7424778{col 50}{space 2} .4073354{col 61}{space 1}   -0.54{col 70}{space 3}0.587{col 78}{space 4} .2530884{col 91}{space 3} 2.178185
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.823637{col 50}{space 2} .6196246{col 61}{space 1}    1.77{col 70}{space 3}0.077{col 78}{space 4} .9363901{col 91}{space 3} 3.551566
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 2.098247{col 50}{space 2} .6497077{col 61}{space 1}    2.39{col 70}{space 3}0.017{col 78}{space 4}    1.143{col 91}{space 3} 3.851829
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.791512{col 50}{space 2} .5055367{col 61}{space 1}    2.07{col 70}{space 3}0.039{col 78}{space 4} 1.029921{col 91}{space 3} 3.116275
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.364938{col 50}{space 2} .3706768{col 61}{space 1}    1.15{col 70}{space 3}0.252{col 78}{space 4} .8011983{col 91}{space 3} 2.325338
{txt}{space 36} {c |}
{space 23}std_RTI_Autor {c |}{col 38}{res}{space 2} 1.403945{col 50}{space 2} .2588639{col 61}{space 1}    1.84{col 70}{space 3}0.066{col 78}{space 4} .9778196{col 91}{space 3} 2.015771
{txt}{space 9}std_offshorable_Blinder2007 {c |}{col 38}{res}{space 2} .9342975{col 50}{space 2} .0757451{col 61}{space 1}   -0.84{col 70}{space 3}0.402{col 78}{space 4} .7969179{col 91}{space 3}  1.09536
{txt}{space 31}_cons {c |}{col 38}{res}{space 2} 1.400063{col 50}{space 2} .5995317{col 61}{space 1}    0.79{col 70}{space 3}0.432{col 78}{space 4} .6043849{col 91}{space 3} 3.243257
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. 
. ******* TABLE 1c ******
. 
. ** Descriptives
. svy: tab aiexposure_gmyrek2023 jobimpact2_AI, row stubw(40)
{txt}(running {bf:tabulate} on estimation sample)
{res}
{txt}{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     4,078
{txt}{col 1}Number of PSUs{col 20}= {res}    4,078{txt}{col 49}Population size{col 67}={res}  4,218.972
{txt}{col 49}Design df{col 67}= {res}     4,077

{txt}{hline 33}{c TT}{hline 49}
Exposure to AI: Simplified       {c |}    Job Impact: Artificial Intelligence (4 Cat   
Groups Based on Gmyrek et al     {c |}                     Version)                    
(2023)                           {c |} No Impac    Worsen   Improve  Don't Kn     Total
{hline 33}{c +}{hline 49}
                    Not Affected {c |}    {res}.5361     .1895      .087     .1873         1
            {txt}Automation Potential {c |}    {res}.3921     .3337     .0424     .2318         1
          {txt}Augmentation Potential {c |}    {res}.4905     .1952     .1106     .2037         1
                     {txt}Big Unknown {c |}    {res}.4351     .2651     .1191     .1807         1
                                 {txt}{c |} 
                           Total {c |}    {res}.4777     .2337     .0956     .1931         1
{txt}{hline 33}{c BT}{hline 49}
  Key:  {col 1}{res}row proportion

{txt}  Pearson:
{col 5}Uncorrected{col 19}chi2({res}9{txt}){col 35}= {res} 100.8101
{txt}{col 5}Design-based{col 19}F({res}8.97{txt}, {res}36583.75{txt}){col 35}= {res}   9.5439{col 51}{txt}P = {res}0.0000
{txt}
{com}. 
. ** Model 7a
. svy: logit AI_worsen i.aiexposure_gmyrek2023 if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}   3{txt},{res}   3959{txt}){col 67}= {res}     16.83
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}  Linearized
{col 1}              AI_worsen{col 25}{c |} Odds Ratio{col 37}   Std. Err.{col 49}      t{col 57}   P>|t|{col 65}     [95% Con{col 78}f. Interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}aiexposure_gmyrek2023 {c |}
{space 2}Automation Potential  {c |}{col 25}{res}{space 2} 2.149197{col 37}{space 2} .2624887{col 48}{space 1}    6.26{col 57}{space 3}0.000{col 65}{space 4} 1.691548{col 78}{space 3} 2.730663
{txt}Augmentation Potential  {c |}{col 25}{res}{space 2} 1.014396{col 37}{space 2} .1403823{col 48}{space 1}    0.10{col 57}{space 3}0.918{col 65}{space 4}  .773346{col 78}{space 3} 1.330581
{txt}{space 11}Big Unknown  {c |}{col 25}{res}{space 2} 1.563634{col 37}{space 2} .1516716{col 48}{space 1}    4.61{col 57}{space 3}0.000{col 65}{space 4} 1.292837{col 78}{space 3} 1.891152
{txt}{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .2369267{col 37}{space 2} .0171015{col 48}{space 1}  -19.95{col 57}{space 3}0.000{col 65}{space 4} .2056625{col 78}{space 3} .2729437
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 7b
. svy: logit AI_worsen i.aiexposure_gmyrek2023 $controls_demographics if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}  15{txt},{res}   3947{txt}){col 67}= {res}      5.62
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} 2.235553{col 50}{space 2} .2810535{col 61}{space 1}    6.40{col 70}{space 3}0.000{col 78}{space 4} 1.747186{col 91}{space 3} 2.860425
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .9647591{col 50}{space 2} .1345981{col 61}{space 1}   -0.26{col 70}{space 3}0.797{col 78}{space 4}  .733884{col 91}{space 3} 1.268266
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.518108{col 50}{space 2} .1493448{col 61}{space 1}    4.24{col 70}{space 3}0.000{col 78}{space 4} 1.251813{col 91}{space 3}  1.84105
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2} .9920165{col 50}{space 2} .0036112{col 61}{space 1}   -2.20{col 70}{space 3}0.028{col 78}{space 4} .9849617{col 91}{space 3} .9991219
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.162724{col 50}{space 2} .1012922{col 61}{space 1}    1.73{col 70}{space 3}0.084{col 78}{space 4} .9801677{col 91}{space 3} 1.379281
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.360552{col 50}{space 2} .1206814{col 61}{space 1}    3.47{col 70}{space 3}0.001{col 78}{space 4} 1.143379{col 91}{space 3} 1.618975
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} .8090219{col 50}{space 2} .0846058{col 61}{space 1}   -2.03{col 70}{space 3}0.043{col 78}{space 4} .6590468{col 91}{space 3} .9931259
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2}  .978504{col 50}{space 2} .0932267{col 61}{space 1}   -0.23{col 70}{space 3}0.820{col 78}{space 4} .8117829{col 91}{space 3} 1.179466
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9325055{col 50}{space 2} .1705051{col 61}{space 1}   -0.38{col 70}{space 3}0.702{col 78}{space 4} .6515757{col 91}{space 3} 1.334559
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9904221{col 50}{space 2} .1431659{col 61}{space 1}   -0.07{col 70}{space 3}0.947{col 78}{space 4} .7460041{col 91}{space 3}  1.31492
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .7737329{col 50}{space 2} .2196408{col 61}{space 1}   -0.90{col 70}{space 3}0.366{col 78}{space 4} .4434914{col 91}{space 3} 1.349885
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.121236{col 50}{space 2} .1860196{col 61}{space 1}    0.69{col 70}{space 3}0.490{col 78}{space 4} .8099059{col 91}{space 3} 1.552242
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.071204{col 50}{space 2} .1729409{col 61}{space 1}    0.43{col 70}{space 3}0.670{col 78}{space 4} .7805624{col 91}{space 3} 1.470066
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.113069{col 50}{space 2} .1739342{col 61}{space 1}    0.69{col 70}{space 3}0.493{col 78}{space 4} .8193474{col 91}{space 3} 1.512085
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.221336{col 50}{space 2} .1905537{col 61}{space 1}    1.28{col 70}{space 3}0.200{col 78}{space 4} .8994758{col 91}{space 3} 1.658367
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2888535{col 50}{space 2} .0666307{col 61}{space 1}   -5.38{col 70}{space 3}0.000{col 78}{space 4} .1837673{col 91}{space 3} .4540325
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 7c
. svy: logit AI_worsen i.aiexposure_gmyrek2023 $controls_demographics $controls_occupindices if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}  17{txt},{res}   3945{txt}){col 67}= {res}      6.32
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} 1.716403{col 50}{space 2} .2412522{col 61}{space 1}    3.84{col 70}{space 3}0.000{col 78}{space 4} 1.302988{col 91}{space 3} 2.260987
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .9613508{col 50}{space 2} .1357471{col 61}{space 1}   -0.28{col 70}{space 3}0.780{col 78}{space 4} .7288725{col 91}{space 3} 1.267979
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2}  1.42011{col 50}{space 2} .1432865{col 61}{space 1}    3.48{col 70}{space 3}0.001{col 78}{space 4} 1.165228{col 91}{space 3} 1.730744
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2} .9925688{col 50}{space 2} .0036279{col 61}{space 1}   -2.04{col 70}{space 3}0.041{col 78}{space 4} .9854816{col 91}{space 3}  .999707
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.116508{col 50}{space 2} .0985759{col 61}{space 1}    1.25{col 70}{space 3}0.212{col 78}{space 4} .9390456{col 91}{space 3} 1.327507
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.351529{col 50}{space 2} .1213379{col 61}{space 1}    3.36{col 70}{space 3}0.001{col 78}{space 4} 1.133398{col 91}{space 3}  1.61164
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} .7916295{col 50}{space 2}  .083682{col 61}{space 1}   -2.21{col 70}{space 3}0.027{col 78}{space 4} .6434505{col 91}{space 3} .9739322
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2}  1.06931{col 50}{space 2} .1034734{col 61}{space 1}    0.69{col 70}{space 3}0.489{col 78}{space 4} .8845264{col 91}{space 3} 1.292697
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9832211{col 50}{space 2} .1787808{col 61}{space 1}   -0.09{col 70}{space 3}0.926{col 78}{space 4} .6883802{col 91}{space 3} 1.404345
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2}  1.04469{col 50}{space 2} .1500768{col 61}{space 1}    0.30{col 70}{space 3}0.761{col 78}{space 4} .7882594{col 91}{space 3}  1.38454
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8267345{col 50}{space 2} .2366536{col 61}{space 1}   -0.66{col 70}{space 3}0.506{col 78}{space 4} .4716657{col 91}{space 3} 1.449098
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.108164{col 50}{space 2} .1847276{col 61}{space 1}    0.62{col 70}{space 3}0.538{col 78}{space 4} .7992226{col 91}{space 3} 1.536527
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.063498{col 50}{space 2} .1726625{col 61}{space 1}    0.38{col 70}{space 3}0.705{col 78}{space 4} .7735694{col 91}{space 3} 1.462091
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.095743{col 50}{space 2} .1728498{col 61}{space 1}    0.58{col 70}{space 3}0.562{col 78}{space 4} .8042542{col 91}{space 3} 1.492877
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.170663{col 50}{space 2} .1860575{col 61}{space 1}    0.99{col 70}{space 3}0.322{col 78}{space 4} .8572474{col 91}{space 3} 1.598666
{txt}{space 36} {c |}
{space 23}std_RTI_Autor {c |}{col 38}{res}{space 2} 1.127482{col 50}{space 2} .0991354{col 61}{space 1}    1.36{col 70}{space 3}0.172{col 78}{space 4} .9489507{col 91}{space 3} 1.339601
{txt}{space 9}std_offshorable_Blinder2007 {c |}{col 38}{res}{space 2} 1.187852{col 50}{space 2} .0554044{col 61}{space 1}    3.69{col 70}{space 3}0.000{col 78}{space 4} 1.084047{col 91}{space 3} 1.301597
{txt}{space 31}_cons {c |}{col 38}{res}{space 2} .3030875{col 50}{space 2} .0698196{col 61}{space 1}   -5.18{col 70}{space 3}0.000{col 78}{space 4} .1929409{col 91}{space 3} .4761149
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. ** Model 8a
. svy: logit AI_improve i.aiexposure_gmyrek2023 if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}   3{txt},{res}   3959{txt}){col 67}= {res}      6.49
{txt}{col 49}Prob > F{col 67}= {res}    0.0002

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}  Linearized
{col 1}             AI_improve{col 25}{c |} Odds Ratio{col 37}   Std. Err.{col 49}      t{col 57}   P>|t|{col 65}     [95% Con{col 78}f. Interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}aiexposure_gmyrek2023 {c |}
{space 2}Automation Potential  {c |}{col 25}{res}{space 2} .4541972{col 37}{space 2} .1216989{col 48}{space 1}   -2.95{col 57}{space 3}0.003{col 65}{space 4} .2685971{col 78}{space 3} .7680466
{txt}Augmentation Potential  {c |}{col 25}{res}{space 2} 1.303874{col 37}{space 2} .2253171{col 48}{space 1}    1.54{col 57}{space 3}0.125{col 65}{space 4}  .929176{col 78}{space 3} 1.829672
{txt}{space 11}Big Unknown  {c |}{col 25}{res}{space 2}  1.34709{col 37}{space 2} .1873183{col 48}{space 1}    2.14{col 57}{space 3}0.032{col 65}{space 4} 1.025646{col 78}{space 3} 1.769277
{txt}{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .0955351{col 37}{space 2} .0097349{col 48}{space 1}  -23.04{col 57}{space 3}0.000{col 65}{space 4} .0782347{col 78}{space 3} .1166611
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 8b
. svy: logit AI_improve i.aiexposure_gmyrek2023 $controls_demographics if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}  15{txt},{res}   3947{txt}){col 67}= {res}     10.30
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                          AI_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .5323391{col 50}{space 2} .1487025{col 61}{space 1}   -2.26{col 70}{space 3}0.024{col 78}{space 4} .3078526{col 91}{space 3} .9205213
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.245286{col 50}{space 2} .2234259{col 61}{space 1}    1.22{col 70}{space 3}0.222{col 78}{space 4} .8759962{col 91}{space 3} 1.770256
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.241423{col 50}{space 2}  .180464{col 61}{space 1}    1.49{col 70}{space 3}0.137{col 78}{space 4} .9335632{col 91}{space 3} 1.650804
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2} .9687858{col 50}{space 2} .0054905{col 61}{space 1}   -5.60{col 70}{space 3}0.000{col 78}{space 4}  .958081{col 91}{space 3} .9796103
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.612239{col 50}{space 2} .2074193{col 61}{space 1}    3.71{col 70}{space 3}0.000{col 78}{space 4} 1.252814{col 91}{space 3} 2.074782
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.994747{col 50}{space 2} .2766637{col 61}{space 1}    4.98{col 70}{space 3}0.000{col 78}{space 4} 1.519825{col 91}{space 3} 2.618077
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} 2.233064{col 50}{space 2} .4570511{col 61}{space 1}    3.93{col 70}{space 3}0.000{col 78}{space 4} 1.494957{col 91}{space 3} 3.335597
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .7028015{col 50}{space 2} .1029811{col 61}{space 1}   -2.41{col 70}{space 3}0.016{col 78}{space 4}  .527313{col 91}{space 3}  .936692
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .6944568{col 50}{space 2} .1972901{col 61}{space 1}   -1.28{col 70}{space 3}0.199{col 78}{space 4} .3978792{col 91}{space 3} 1.212102
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.094834{col 50}{space 2}  .244923{col 61}{space 1}    0.41{col 70}{space 3}0.685{col 78}{space 4}  .706106{col 91}{space 3} 1.697568
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.050497{col 50}{space 2} .5469275{col 61}{space 1}    0.09{col 70}{space 3}0.925{col 78}{space 4} .3785229{col 91}{space 3} 2.915393
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5319708{col 50}{space 2} .1565196{col 61}{space 1}   -2.15{col 70}{space 3}0.032{col 78}{space 4} .2987897{col 91}{space 3} .9471306
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .4849458{col 50}{space 2} .1328014{col 61}{space 1}   -2.64{col 70}{space 3}0.008{col 78}{space 4} .2834798{col 91}{space 3} .8295914
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .5812188{col 50}{space 2} .1404141{col 61}{space 1}   -2.25{col 70}{space 3}0.025{col 78}{space 4} .3619419{col 91}{space 3} .9333414
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .8512307{col 50}{space 2} .1962051{col 61}{space 1}   -0.70{col 70}{space 3}0.485{col 78}{space 4} .5417375{col 91}{space 3} 1.337536
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .1618173{col 50}{space 2} .0605233{col 61}{space 1}   -4.87{col 70}{space 3}0.000{col 78}{space 4} .0777247{col 91}{space 3} .3368921
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 8c
. svy: logit AI_improve i.aiexposure_gmyrek2023 $controls_demographics $controls_occupindices if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}  17{txt},{res}   3945{txt}){col 67}= {res}     10.28
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                          AI_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .4799791{col 50}{space 2}   .13675{col 61}{space 1}   -2.58{col 70}{space 3}0.010{col 78}{space 4} .2745578{col 91}{space 3} .8390945
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.112817{col 50}{space 2} .2042148{col 61}{space 1}    0.58{col 70}{space 3}0.560{col 78}{space 4} .7765521{col 91}{space 3} 1.594692
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.239504{col 50}{space 2} .1825589{col 61}{space 1}    1.46{col 70}{space 3}0.145{col 78}{space 4} .9286275{col 91}{space 3} 1.654453
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2}  .969126{col 50}{space 2} .0055327{col 61}{space 1}   -5.49{col 70}{space 3}0.000{col 78}{space 4} .9583394{col 91}{space 3} .9800341
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.554656{col 50}{space 2} .2029459{col 61}{space 1}    3.38{col 70}{space 3}0.001{col 78}{space 4} 1.203604{col 91}{space 3} 2.008097
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  1.83961{col 50}{space 2} .2598538{col 61}{space 1}    4.32{col 70}{space 3}0.000{col 78}{space 4}  1.39461{col 91}{space 3} 2.426604
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2}  2.03321{col 50}{space 2} .4195004{col 61}{space 1}    3.44{col 70}{space 3}0.001{col 78}{space 4} 1.356766{col 91}{space 3} 3.046908
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .7646578{col 50}{space 2} .1162988{col 61}{space 1}   -1.76{col 70}{space 3}0.078{col 78}{space 4} .5675003{col 91}{space 3} 1.030311
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .7252594{col 50}{space 2} .2088231{col 61}{space 1}   -1.12{col 70}{space 3}0.265{col 78}{space 4} .4124135{col 91}{space 3} 1.275422
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.102247{col 50}{space 2} .2484272{col 61}{space 1}    0.43{col 70}{space 3}0.666{col 78}{space 4} .7085563{col 91}{space 3} 1.714682
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.185814{col 50}{space 2} .6200118{col 61}{space 1}    0.33{col 70}{space 3}0.744{col 78}{space 4} .4254254{col 91}{space 3} 3.305292
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5248647{col 50}{space 2} .1548365{col 61}{space 1}   -2.19{col 70}{space 3}0.029{col 78}{space 4} .2943499{col 91}{space 3}  .935903
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .4632377{col 50}{space 2} .1276822{col 61}{space 1}   -2.79{col 70}{space 3}0.005{col 78}{space 4} .2698457{col 91}{space 3}  .795229
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .5428763{col 50}{space 2} .1330133{col 61}{space 1}   -2.49{col 70}{space 3}0.013{col 78}{space 4} .3357988{col 91}{space 3} .8776524
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .7368803{col 50}{space 2} .1723107{col 61}{space 1}   -1.31{col 70}{space 3}0.192{col 78}{space 4}    .4659{col 91}{space 3}  1.16547
{txt}{space 36} {c |}
{space 23}std_RTI_Autor {c |}{col 38}{res}{space 2}  .736434{col 50}{space 2} .1093962{col 61}{space 1}   -2.06{col 70}{space 3}0.040{col 78}{space 4} .5503645{col 91}{space 3} .9854106
{txt}{space 9}std_offshorable_Blinder2007 {c |}{col 38}{res}{space 2} 1.276314{col 50}{space 2} .0789251{col 61}{space 1}    3.95{col 70}{space 3}0.000{col 78}{space 4} 1.130589{col 91}{space 3} 1.440823
{txt}{space 31}_cons {c |}{col 38}{res}{space 2} .1853588{col 50}{space 2} .0705107{col 61}{space 1}   -4.43{col 70}{space 3}0.000{col 78}{space 4} .0879258{col 91}{space 3}   .39076
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. ** Model 9a
. svy: logit AI_directionworse ib3.aiexposure_gmyrek2023 if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}   3{txt},{res}   1319{txt}){col 67}= {res}      8.90
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}  Linearized
{col 1}    AI_directionworse{col 23}{c |} Odds Ratio{col 35}   Std. Err.{col 47}      t{col 55}   P>|t|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
aiexposure_gmyrek2023 {c |}
{space 8}Not Affected  {c |}{col 23}{res}{space 2} 1.255641{col 35}{space 2} .2589383{col 46}{space 1}    1.10{col 55}{space 3}0.270{col 63}{space 4} .8378563{col 76}{space 3} 1.881747
{txt}Automation Potential  {c |}{col 23}{res}{space 2} 4.637831{col 35}{space 2} 1.423927{col 46}{space 1}    5.00{col 55}{space 3}0.000{col 63}{space 4} 2.539427{col 76}{space 3} 8.470209
{txt}{space 9}Big Unknown  {c |}{col 23}{res}{space 2} 1.355281{col 35}{space 2} .2705414{col 46}{space 1}    1.52{col 55}{space 3}0.128{col 63}{space 4} .9161292{col 76}{space 3} 2.004944
{txt}{space 21} {c |}
{space 16}_cons {c |}{col 23}{res}{space 2} 1.749315{col 35}{space 2}  .297136{col 46}{space 1}    3.29{col 55}{space 3}0.001{col 63}{space 4} 1.253579{col 76}{space 3} 2.441094
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 9b
. svy: logit AI_directionworse ib3.aiexposure_gmyrek2023 $controls_demographics if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}  15{txt},{res}   1307{txt}){col 67}= {res}      5.59
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                   AI_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}aiexposure_gmyrek2023 {c |}
{space 23}Not Affected  {c |}{col 38}{res}{space 2} 1.162931{col 50}{space 2} .2487439{col 61}{space 1}    0.71{col 70}{space 3}0.481{col 78}{space 4} .7643994{col 91}{space 3} 1.769245
{txt}{space 15}Automation Potential  {c |}{col 38}{res}{space 2}   3.4273{col 50}{space 2} 1.077631{col 61}{space 1}    3.92{col 70}{space 3}0.000{col 78}{space 4} 1.849554{col 91}{space 3} 6.350929
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.192179{col 50}{space 2} .2487263{col 61}{space 1}    0.84{col 70}{space 3}0.400{col 78}{space 4} .7917553{col 91}{space 3} 1.795114
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2} 1.021116{col 50}{space 2} .0063223{col 61}{space 1}    3.37{col 70}{space 3}0.001{col 78}{space 4} 1.008788{col 91}{space 3} 1.033595
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} .7479096{col 50}{space 2} .1096777{col 61}{space 1}   -1.98{col 70}{space 3}0.048{col 78}{space 4} .5609312{col 91}{space 3} .9972145
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .6906185{col 50}{space 2} .1055439{col 61}{space 1}   -2.42{col 70}{space 3}0.016{col 78}{space 4} .5117218{col 91}{space 3}  .932057
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} .4114719{col 50}{space 2} .0936857{col 61}{space 1}   -3.90{col 70}{space 3}0.000{col 78}{space 4} .2632432{col 91}{space 3} .6431663
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.302644{col 50}{space 2} .2152886{col 61}{space 1}    1.60{col 70}{space 3}0.110{col 78}{space 4} .9419292{col 91}{space 3} 1.801496
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.273158{col 50}{space 2} .4239843{col 61}{space 1}    0.73{col 70}{space 3}0.468{col 78}{space 4} .6624557{col 91}{space 3} 2.446851
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9312545{col 50}{space 2} .2473848{col 61}{space 1}   -0.27{col 70}{space 3}0.789{col 78}{space 4} .5530216{col 91}{space 3} 1.568176
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .7078748{col 50}{space 2} .4404368{col 61}{space 1}   -0.56{col 70}{space 3}0.579{col 78}{space 4} .2088607{col 91}{space 3} 2.399144
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.669033{col 50}{space 2} .5691417{col 61}{space 1}    1.50{col 70}{space 3}0.133{col 78}{space 4} .8549455{col 91}{space 3} 3.258304
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.908685{col 50}{space 2} .6007226{col 61}{space 1}    2.05{col 70}{space 3}0.040{col 78}{space 4} 1.029411{col 91}{space 3} 3.538992
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.635793{col 50}{space 2} .4600001{col 61}{space 1}    1.75{col 70}{space 3}0.080{col 78}{space 4} .9421996{col 91}{space 3} 2.839969
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.244886{col 50}{space 2} .3377145{col 61}{space 1}    0.81{col 70}{space 3}0.420{col 78}{space 4} .7311437{col 91}{space 3} 2.119613
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 1.653349{col 50}{space 2} .7036526{col 61}{space 1}    1.18{col 70}{space 3}0.238{col 78}{space 4} .7174104{col 91}{space 3} 3.810321
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 9c
. svy: logit AI_directionworse ib3.aiexposure_gmyrek2023 $controls_demographics $controls_occupindices if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}  17{txt},{res}   1305{txt}){col 67}= {res}      5.22
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                   AI_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}aiexposure_gmyrek2023 {c |}
{space 23}Not Affected  {c |}{col 38}{res}{space 2} 1.095452{col 50}{space 2} .2361537{col 61}{space 1}    0.42{col 70}{space 3}0.672{col 78}{space 4} .7176722{col 91}{space 3} 1.672094
{txt}{space 15}Automation Potential  {c |}{col 38}{res}{space 2} 3.004289{col 50}{space 2} .9782146{col 61}{space 1}    3.38{col 70}{space 3}0.001{col 78}{space 4} 1.586102{col 91}{space 3} 5.690524
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.067489{col 50}{space 2} .2290692{col 61}{space 1}    0.30{col 70}{space 3}0.761{col 78}{space 4} .7007115{col 91}{space 3} 1.626251
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2} 1.022007{col 50}{space 2} .0064316{col 61}{space 1}    3.46{col 70}{space 3}0.001{col 78}{space 4} 1.009468{col 91}{space 3} 1.034703
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} .7628162{col 50}{space 2} .1117754{col 61}{space 1}   -1.85{col 70}{space 3}0.065{col 78}{space 4} .5722411{col 91}{space 3} 1.016859
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7353778{col 50}{space 2} .1151349{col 61}{space 1}   -1.96{col 70}{space 3}0.050{col 78}{space 4} .5409029{col 91}{space 3} .9997736
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} .4415403{col 50}{space 2} .1016079{col 61}{space 1}   -3.55{col 70}{space 3}0.000{col 78}{space 4} .2811325{col 91}{space 3} .6934732
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.293344{col 50}{space 2} .2240005{col 61}{space 1}    1.49{col 70}{space 3}0.138{col 78}{space 4}  .920778{col 91}{space 3} 1.816657
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}  1.25701{col 50}{space 2} .4161744{col 61}{space 1}    0.69{col 70}{space 3}0.490{col 78}{space 4} .6565416{col 91}{space 3} 2.406664
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.012727{col 50}{space 2}  .268786{col 61}{space 1}    0.05{col 70}{space 3}0.962{col 78}{space 4} .6016856{col 91}{space 3} 1.704572
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2}  .686056{col 50}{space 2} .4217752{col 61}{space 1}   -0.61{col 70}{space 3}0.540{col 78}{space 4} .2053884{col 91}{space 3} 2.291623
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.689433{col 50}{space 2} .5707858{col 61}{space 1}    1.55{col 70}{space 3}0.121{col 78}{space 4} .8707495{col 91}{space 3} 3.277846
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.972542{col 50}{space 2} .6154734{col 61}{space 1}    2.18{col 70}{space 3}0.030{col 78}{space 4} 1.069524{col 91}{space 3} 3.637995
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.736293{col 50}{space 2} .4882026{col 61}{space 1}    1.96{col 70}{space 3}0.050{col 78}{space 4} 1.000154{col 91}{space 3}  3.01425
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2}  1.37574{col 50}{space 2} .3704561{col 61}{space 1}    1.18{col 70}{space 3}0.236{col 78}{space 4} .8111785{col 91}{space 3} 2.333222
{txt}{space 36} {c |}
{space 23}std_RTI_Autor {c |}{col 38}{res}{space 2} 1.424456{col 50}{space 2} .2338757{col 61}{space 1}    2.15{col 70}{space 3}0.031{col 78}{space 4} 1.032204{col 91}{space 3}  1.96577
{txt}{space 9}std_offshorable_Blinder2007 {c |}{col 38}{res}{space 2} .9027133{col 50}{space 2} .0648006{col 61}{space 1}   -1.43{col 70}{space 3}0.154{col 78}{space 4} .7841353{col 91}{space 3} 1.039223
{txt}{space 31}_cons {c |}{col 38}{res}{space 2} 1.539998{col 50}{space 2} .6672614{col 61}{space 1}    1.00{col 70}{space 3}0.319{col 78}{space 4} .6582141{col 91}{space 3} 3.603073
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. 
. 
. 
. ************************************************************************************************************
. 
. * TABLE 2: Demographic Predictors of Subjective Exposure to AI (Raw Data) *
.         
. * Set Controls and Sample Size  
. global controls_demographics i.age_3 i.female_dummy i.degree_dummy i.employmentstatus5 i.employsector5 i.equivincomeHHquintile_HBAI_imp i.isco08_5group
{txt}
{com}. global controls_objective aiexposure_felten2021 i.aiexposure_pizzinelli2023 i.aiexposure_gmyrek2023
{txt}
{com}. 
. svy: logit AI_worsen aiexposure_felten2021  $controls_demographics $controls_objective, or
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}note: aiexposure_felten2021 omitted because of collinearity

Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      4.36
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.132371{col 50}{space 2} .1272744{col 61}{space 1}    1.11{col 70}{space 3}0.269{col 78}{space 4} .9084218{col 91}{space 3} 1.411529
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .9121425{col 50}{space 2}  .111979{col 61}{space 1}   -0.75{col 70}{space 3}0.454{col 78}{space 4} .7170231{col 91}{space 3} 1.160359
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .7963834{col 50}{space 2}  .107791{col 61}{space 1}   -1.68{col 70}{space 3}0.093{col 78}{space 4} .6107681{col 91}{space 3} 1.038408
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .8775447{col 50}{space 2} .0779128{col 61}{space 1}   -1.47{col 70}{space 3}0.141{col 78}{space 4} .7373476{col 91}{space 3} 1.044399
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  1.26827{col 50}{space 2}  .116173{col 61}{space 1}    2.59{col 70}{space 3}0.010{col 78}{space 4} 1.059786{col 91}{space 3} 1.517768
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.270618{col 50}{space 2} .1350353{col 61}{space 1}    2.25{col 70}{space 3}0.024{col 78}{space 4} 1.031634{col 91}{space 3} 1.564964
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9688247{col 50}{space 2} .0945639{col 61}{space 1}   -0.32{col 70}{space 3}0.746{col 78}{space 4} .8000861{col 91}{space 3}  1.17315
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9331544{col 50}{space 2} .1706257{col 61}{space 1}   -0.38{col 70}{space 3}0.705{col 78}{space 4} .6520264{col 91}{space 3} 1.335494
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9994992{col 50}{space 2} .1454519{col 61}{space 1}   -0.00{col 70}{space 3}0.997{col 78}{space 4} .7514043{col 91}{space 3} 1.329509
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8543309{col 50}{space 2} .2446505{col 61}{space 1}   -0.55{col 70}{space 3}0.583{col 78}{space 4} .4873009{col 91}{space 3} 1.497804
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}   1.0992{col 50}{space 2} .1850128{col 61}{space 1}    0.56{col 70}{space 3}0.574{col 78}{space 4} .7902459{col 91}{space 3} 1.528943
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.003071{col 50}{space 2} .1648735{col 61}{space 1}    0.02{col 70}{space 3}0.985{col 78}{space 4}  .726738{col 91}{space 3} 1.384476
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.023778{col 50}{space 2} .1646043{col 61}{space 1}    0.15{col 70}{space 3}0.884{col 78}{space 4} .7469762{col 91}{space 3} 1.403153
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.060449{col 50}{space 2} .1731385{col 61}{space 1}    0.36{col 70}{space 3}0.719{col 78}{space 4} .7699674{col 91}{space 3} 1.460519
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}   .94564{col 50}{space 2} .1172553{col 61}{space 1}   -0.45{col 70}{space 3}0.652{col 78}{space 4} .7415634{col 91}{space 3} 1.205878
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.000113{col 50}{space 2} .1883523{col 61}{space 1}    0.00{col 70}{space 3}1.000{col 78}{space 4} .6913415{col 91}{space 3} 1.446791
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .7392922{col 50}{space 2} .1278909{col 61}{space 1}   -1.75{col 70}{space 3}0.081{col 78}{space 4} .5266486{col 91}{space 3} 1.037794
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .8875048{col 50}{space 2} .2078441{col 61}{space 1}   -0.51{col 70}{space 3}0.610{col 78}{space 4} .5607477{col 91}{space 3} 1.404669
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2}        1{col 50}{txt}  (omitted)
{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.293345{col 50}{space 2} .3083182{col 61}{space 1}    1.08{col 70}{space 3}0.281{col 78}{space 4} .8104681{col 91}{space 3}  2.06392
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.012834{col 50}{space 2} .2390897{col 61}{space 1}    0.05{col 70}{space 3}0.957{col 78}{space 4} .6375913{col 91}{space 3}  1.60892
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2}  1.47375{col 50}{space 2} .3200393{col 61}{space 1}    1.79{col 70}{space 3}0.074{col 78}{space 4} .9627655{col 91}{space 3} 2.255938
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .8816561{col 50}{space 2}   .12698{col 61}{space 1}   -0.87{col 70}{space 3}0.382{col 78}{space 4} .6647648{col 91}{space 3} 1.169312
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.180469{col 50}{space 2} .1459234{col 61}{space 1}    1.34{col 70}{space 3}0.180{col 78}{space 4} .9264062{col 91}{space 3} 1.504208
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2431594{col 50}{space 2} .0630194{col 61}{space 1}   -5.46{col 70}{space 3}0.000{col 78}{space 4} .1462913{col 91}{space 3} .4041697
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. generate infullmodel_Table2 = e(sample) 
{txt}
{com}.          
.          
. ******* Age Group ********
. 
. * Model 1: Worsen 
. svy: logit AI_worsen ib3.age_3 if infullmodel_Table2 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   2{txt},{res}   3961{txt}){col 67}= {res}      2.96
{txt}{col 49}Prob > F{col 67}= {res}    0.0518

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}   AI_worsen{col 14}{c |} Odds Ratio{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}age_3group {c |}
{space 6}18-29  {c |}{col 14}{res}{space 2} 1.305605{col 26}{space 2} .1669743{col 37}{space 1}    2.09{col 46}{space 3}0.037{col 54}{space 4} 1.016057{col 67}{space 3} 1.677666
{txt}{space 6}30-49  {c |}{col 14}{res}{space 2}  1.19893{col 26}{space 2} .1071751{col 37}{space 1}    2.03{col 46}{space 3}0.042{col 54}{space 4}  1.00619{col 67}{space 3} 1.428591
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} .2698467{col 26}{space 2} .0189056{col 37}{space 1}  -18.70{col 46}{space 3}0.000{col 54}{space 4} .2352139{col 67}{space 3} .3095787
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_worsen ib3.age_3 $controls_demographics if infullmodel_Table2 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      4.27
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}age_3group {c |}
{space 30}18-29  {c |}{col 38}{res}{space 2} 1.288983{col 50}{space 2} .1749154{col 61}{space 1}    1.87{col 70}{space 3}0.061{col 78}{space 4} .9878788{col 91}{space 3} 1.681864
{txt}{space 30}30-49  {c |}{col 38}{res}{space 2} 1.150756{col 50}{space 2} .1080209{col 61}{space 1}    1.50{col 70}{space 3}0.135{col 78}{space 4} .9573198{col 91}{space 3} 1.383278
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}  .870817{col 50}{space 2} .0765998{col 61}{space 1}   -1.57{col 70}{space 3}0.116{col 78}{space 4} .7328747{col 91}{space 3} 1.034723
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.282467{col 50}{space 2} .1163864{col 61}{space 1}    2.74{col 70}{space 3}0.006{col 78}{space 4} 1.073431{col 91}{space 3} 1.532209
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.279242{col 50}{space 2}  .134588{col 61}{space 1}    2.34{col 70}{space 3}0.019{col 78}{space 4}  1.04081{col 91}{space 3} 1.572296
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9047129{col 50}{space 2} .0865865{col 61}{space 1}   -1.05{col 70}{space 3}0.295{col 78}{space 4}   .74993{col 91}{space 3} 1.091442
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .8892559{col 50}{space 2} .1604124{col 61}{space 1}   -0.65{col 70}{space 3}0.515{col 78}{space 4} .6243557{col 91}{space 3} 1.266548
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9881668{col 50}{space 2} .1427731{col 61}{space 1}   -0.08{col 70}{space 3}0.934{col 78}{space 4} .7444041{col 91}{space 3} 1.311752
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8492572{col 50}{space 2} .2455706{col 61}{space 1}   -0.57{col 70}{space 3}0.572{col 78}{space 4} .4817605{col 91}{space 3} 1.497088
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.111529{col 50}{space 2} .1852479{col 61}{space 1}    0.63{col 70}{space 3}0.526{col 78}{space 4}  .801707{col 91}{space 3} 1.541082
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .9992331{col 50}{space 2} .1627154{col 61}{space 1}   -0.00{col 70}{space 3}0.996{col 78}{space 4} .7261303{col 91}{space 3} 1.375052
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.011691{col 50}{space 2} .1605068{col 61}{space 1}    0.07{col 70}{space 3}0.942{col 78}{space 4} .7412453{col 91}{space 3}  1.38081
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.081836{col 50}{space 2} .1754481{col 61}{space 1}    0.49{col 70}{space 3}0.628{col 78}{space 4} .7871807{col 91}{space 3} 1.486785
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.059863{col 50}{space 2} .1222794{col 61}{space 1}    0.50{col 70}{space 3}0.614{col 78}{space 4} .8453065{col 91}{space 3} 1.328878
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.498164{col 50}{space 2} .1787707{col 61}{space 1}    3.39{col 70}{space 3}0.001{col 78}{space 4} 1.185652{col 91}{space 3} 1.893046
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .6336916{col 50}{space 2} .0949955{col 61}{space 1}   -3.04{col 70}{space 3}0.002{col 78}{space 4} .4723206{col 91}{space 3} .8501958
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}  .599223{col 50}{space 2} .1024432{col 61}{space 1}   -3.00{col 70}{space 3}0.003{col 78}{space 4} .4285707{col 91}{space 3} .8378272
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2597675{col 50}{space 2} .0460782{col 61}{space 1}   -7.60{col 70}{space 3}0.000{col 78}{space 4} .1834638{col 91}{space 3} .3678062
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_worsen ib3.age_3 $controls_demographics  $controls_objective if infullmodel_Table2 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      4.36
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}age_3group {c |}
{space 30}18-29  {c |}{col 38}{res}{space 2} 1.255677{col 50}{space 2} .1699566{col 61}{space 1}    1.68{col 70}{space 3}0.093{col 78}{space 4} .9630124{col 91}{space 3} 1.637283
{txt}{space 30}30-49  {c |}{col 38}{res}{space 2} 1.145356{col 50}{space 2} .1084957{col 61}{space 1}    1.43{col 70}{space 3}0.152{col 78}{space 4} .9512274{col 91}{space 3} 1.379103
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .8775447{col 50}{space 2} .0779128{col 61}{space 1}   -1.47{col 70}{space 3}0.141{col 78}{space 4} .7373476{col 91}{space 3} 1.044399
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  1.26827{col 50}{space 2}  .116173{col 61}{space 1}    2.59{col 70}{space 3}0.010{col 78}{space 4} 1.059786{col 91}{space 3} 1.517768
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.270618{col 50}{space 2} .1350353{col 61}{space 1}    2.25{col 70}{space 3}0.024{col 78}{space 4} 1.031634{col 91}{space 3} 1.564964
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9688247{col 50}{space 2} .0945639{col 61}{space 1}   -0.32{col 70}{space 3}0.746{col 78}{space 4} .8000861{col 91}{space 3}  1.17315
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9331544{col 50}{space 2} .1706257{col 61}{space 1}   -0.38{col 70}{space 3}0.705{col 78}{space 4} .6520264{col 91}{space 3} 1.335494
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9994992{col 50}{space 2} .1454519{col 61}{space 1}   -0.00{col 70}{space 3}0.997{col 78}{space 4} .7514043{col 91}{space 3} 1.329509
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8543309{col 50}{space 2} .2446505{col 61}{space 1}   -0.55{col 70}{space 3}0.583{col 78}{space 4} .4873009{col 91}{space 3} 1.497804
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}   1.0992{col 50}{space 2} .1850128{col 61}{space 1}    0.56{col 70}{space 3}0.574{col 78}{space 4} .7902459{col 91}{space 3} 1.528943
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.003071{col 50}{space 2} .1648735{col 61}{space 1}    0.02{col 70}{space 3}0.985{col 78}{space 4}  .726738{col 91}{space 3} 1.384476
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.023778{col 50}{space 2} .1646043{col 61}{space 1}    0.15{col 70}{space 3}0.884{col 78}{space 4} .7469762{col 91}{space 3} 1.403153
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.060449{col 50}{space 2} .1731385{col 61}{space 1}    0.36{col 70}{space 3}0.719{col 78}{space 4} .7699674{col 91}{space 3} 1.460519
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}   .94564{col 50}{space 2} .1172553{col 61}{space 1}   -0.45{col 70}{space 3}0.652{col 78}{space 4} .7415634{col 91}{space 3} 1.205878
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.000113{col 50}{space 2} .1883523{col 61}{space 1}    0.00{col 70}{space 3}1.000{col 78}{space 4} .6913415{col 91}{space 3} 1.446791
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .7392922{col 50}{space 2} .1278909{col 61}{space 1}   -1.75{col 70}{space 3}0.081{col 78}{space 4} .5266486{col 91}{space 3} 1.037794
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .8875048{col 50}{space 2} .2078441{col 61}{space 1}   -0.51{col 70}{space 3}0.610{col 78}{space 4} .5607477{col 91}{space 3} 1.404669
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.132371{col 50}{space 2} .1272744{col 61}{space 1}    1.11{col 70}{space 3}0.269{col 78}{space 4} .9084218{col 91}{space 3} 1.411529
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.293345{col 50}{space 2} .3083182{col 61}{space 1}    1.08{col 70}{space 3}0.281{col 78}{space 4} .8104681{col 91}{space 3}  2.06392
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.012834{col 50}{space 2} .2390897{col 61}{space 1}    0.05{col 70}{space 3}0.957{col 78}{space 4} .6375913{col 91}{space 3}  1.60892
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2}  1.47375{col 50}{space 2} .3200393{col 61}{space 1}    1.79{col 70}{space 3}0.074{col 78}{space 4} .9627655{col 91}{space 3} 2.255938
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .8816561{col 50}{space 2}   .12698{col 61}{space 1}   -0.87{col 70}{space 3}0.382{col 78}{space 4} .6647648{col 91}{space 3} 1.169312
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.180469{col 50}{space 2} .1459234{col 61}{space 1}    1.34{col 70}{space 3}0.180{col 78}{space 4} .9264062{col 91}{space 3} 1.504208
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .1936482{col 50}{space 2}  .049402{col 61}{space 1}   -6.44{col 70}{space 3}0.000{col 78}{space 4} .1174343{col 91}{space 3} .3193242
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 2: Improve 
. svy: logit AI_improve ib3.age_3 if infullmodel_Table2 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   2{txt},{res}   3961{txt}){col 67}= {res}     15.89
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}  AI_improve{col 14}{c |} Odds Ratio{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}age_3group {c |}
{space 6}18-29  {c |}{col 14}{res}{space 2} 2.742206{col 26}{space 2} .4994177{col 37}{space 1}    5.54{col 46}{space 3}0.000{col 54}{space 4}   1.9188{col 67}{space 3} 3.918957
{txt}{space 6}30-49  {c |}{col 14}{res}{space 2} 1.789594{col 26}{space 2} .2620354{col 37}{space 1}    3.97{col 46}{space 3}0.000{col 54}{space 4} 1.343018{col 67}{space 3} 2.384663
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} .0626806{col 26}{space 2} .0076591{col 37}{space 1}  -22.67{col 46}{space 3}0.000{col 54}{space 4} .0493278{col 67}{space 3}  .079648
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_improve ib3.age_3 $controls_demographics if infullmodel_Table2 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      9.00
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                          AI_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}age_3group {c |}
{space 30}18-29  {c |}{col 38}{res}{space 2} 2.318133{col 50}{space 2} .4561616{col 61}{space 1}    4.27{col 70}{space 3}0.000{col 78}{space 4} 1.576116{col 91}{space 3} 3.409485
{txt}{space 30}30-49  {c |}{col 38}{res}{space 2} 1.358249{col 50}{space 2}  .214506{col 61}{space 1}    1.94{col 70}{space 3}0.053{col 78}{space 4} .9965741{col 91}{space 3} 1.851183
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6274358{col 50}{space 2} .0815649{col 61}{space 1}   -3.59{col 70}{space 3}0.000{col 78}{space 4} .4862747{col 91}{space 3} .8095748
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.753261{col 50}{space 2} .2386836{col 61}{space 1}    4.12{col 70}{space 3}0.000{col 78}{space 4} 1.342552{col 91}{space 3} 2.289613
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .4766213{col 50}{space 2} .0975186{col 61}{space 1}   -3.62{col 70}{space 3}0.000{col 78}{space 4} .3191252{col 91}{space 3} .7118456
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2}  .703992{col 50}{space 2} .1022251{col 61}{space 1}   -2.42{col 70}{space 3}0.016{col 78}{space 4} .5295767{col 91}{space 3} .9358507
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .6980172{col 50}{space 2} .1996649{col 61}{space 1}   -1.26{col 70}{space 3}0.209{col 78}{space 4} .3983906{col 91}{space 3} 1.222991
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.045743{col 50}{space 2} .2381017{col 61}{space 1}    0.20{col 70}{space 3}0.844{col 78}{space 4} .6692037{col 91}{space 3} 1.634148
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.253748{col 50}{space 2} .6701505{col 61}{space 1}    0.42{col 70}{space 3}0.672{col 78}{space 4} .4396317{col 91}{space 3} 3.575457
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5114603{col 50}{space 2} .1552909{col 61}{space 1}   -2.21{col 70}{space 3}0.027{col 78}{space 4} .2820259{col 91}{space 3} .9275449
{txt}{space 34}3  {c |}{col 38}{res}{space 2}  .431203{col 50}{space 2} .1200384{col 61}{space 1}   -3.02{col 70}{space 3}0.003{col 78}{space 4} .2498341{col 91}{space 3} .7442382
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .4891384{col 50}{space 2} .1216417{col 61}{space 1}   -2.88{col 70}{space 3}0.004{col 78}{space 4}   .30039{col 91}{space 3} .7964859
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .6643646{col 50}{space 2} .1598227{col 61}{space 1}   -1.70{col 70}{space 3}0.089{col 78}{space 4} .4145481{col 91}{space 3} 1.064727
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .8602597{col 50}{space 2} .1431378{col 61}{space 1}   -0.90{col 70}{space 3}0.366{col 78}{space 4} .6208056{col 91}{space 3} 1.192075
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}  .483637{col 50}{space 2} .1091257{col 61}{space 1}   -3.22{col 70}{space 3}0.001{col 78}{space 4} .3107417{col 91}{space 3} .7527305
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .4243676{col 50}{space 2} .1168096{col 61}{space 1}   -3.11{col 70}{space 3}0.002{col 78}{space 4} .2473845{col 91}{space 3} .7279674
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .3840638{col 50}{space 2} .1048289{col 61}{space 1}   -3.51{col 70}{space 3}0.000{col 78}{space 4} .2249053{col 91}{space 3} .6558537
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2036033{col 50}{space 2}  .054992{col 61}{space 1}   -5.89{col 70}{space 3}0.000{col 78}{space 4} .1198976{col 91}{space 3} .3457477
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_improve ib3.age_3 $controls_demographics $controls_objective if infullmodel_Table2 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      7.85
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                          AI_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}age_3group {c |}
{space 30}18-29  {c |}{col 38}{res}{space 2} 2.277934{col 50}{space 2} .4508852{col 61}{space 1}    4.16{col 70}{space 3}0.000{col 78}{space 4} 1.545276{col 91}{space 3} 3.357964
{txt}{space 30}30-49  {c |}{col 38}{res}{space 2} 1.359409{col 50}{space 2} .2158074{col 61}{space 1}    1.93{col 70}{space 3}0.053{col 78}{space 4} .9958173{col 91}{space 3} 1.855754
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6317683{col 50}{space 2} .0833817{col 61}{space 1}   -3.48{col 70}{space 3}0.001{col 78}{space 4} .4877314{col 91}{space 3} .8183422
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.732196{col 50}{space 2} .2368375{col 61}{space 1}    4.02{col 70}{space 3}0.000{col 78}{space 4} 1.324889{col 91}{space 3} 2.264721
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .4842575{col 50}{space 2} .0992227{col 61}{space 1}   -3.54{col 70}{space 3}0.000{col 78}{space 4} .3240521{col 91}{space 3} .7236656
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .6974604{col 50}{space 2} .1028677{col 61}{space 1}   -2.44{col 70}{space 3}0.015{col 78}{space 4} .5223221{col 91}{space 3} .9313239
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .6836831{col 50}{space 2} .1929683{col 61}{space 1}   -1.35{col 70}{space 3}0.178{col 78}{space 4} .3931256{col 91}{space 3}  1.18899
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.129698{col 50}{space 2} .2598135{col 61}{space 1}    0.53{col 70}{space 3}0.596{col 78}{space 4} .7196789{col 91}{space 3} 1.773314
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.339877{col 50}{space 2} .7204718{col 61}{space 1}    0.54{col 70}{space 3}0.586{col 78}{space 4} .4668973{col 91}{space 3} 3.845108
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5083028{col 50}{space 2} .1523285{col 61}{space 1}   -2.26{col 70}{space 3}0.024{col 78}{space 4} .2824594{col 91}{space 3}  .914722
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .4113217{col 50}{space 2} .1135379{col 61}{space 1}   -3.22{col 70}{space 3}0.001{col 78}{space 4} .2394149{col 91}{space 3} .7066626
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .4648291{col 50}{space 2} .1149079{col 61}{space 1}   -3.10{col 70}{space 3}0.002{col 78}{space 4} .2862912{col 91}{space 3} .7547074
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .6062726{col 50}{space 2} .1449004{col 61}{space 1}   -2.09{col 70}{space 3}0.036{col 78}{space 4} .3794607{col 91}{space 3} .9686549
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.010877{col 50}{space 2} .1838329{col 61}{space 1}    0.06{col 70}{space 3}0.953{col 78}{space 4} .7077112{col 91}{space 3} 1.443913
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.090424{col 50}{space 2} .3315547{col 61}{space 1}    0.28{col 70}{space 3}0.776{col 78}{space 4} .6007582{col 91}{space 3} 1.979207
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .7050556{col 50}{space 2} .2294216{col 61}{space 1}   -1.07{col 70}{space 3}0.283{col 78}{space 4} .3725306{col 91}{space 3} 1.334396
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}  .859264{col 50}{space 2} .2912932{col 61}{space 1}   -0.45{col 70}{space 3}0.655{col 78}{space 4} .4420572{col 91}{space 3} 1.670224
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.600942{col 50}{space 2} .2810704{col 61}{space 1}    2.68{col 70}{space 3}0.007{col 78}{space 4} 1.134718{col 91}{space 3} 2.258723
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.005969{col 50}{space 2} .3909627{col 61}{space 1}    0.02{col 70}{space 3}0.988{col 78}{space 4} .4695373{col 91}{space 3} 2.155257
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2}  1.09556{col 50}{space 2} .4040863{col 61}{space 1}    0.25{col 70}{space 3}0.805{col 78}{space 4} .5315977{col 91}{space 3}  2.25782
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .3170697{col 50}{space 2}  .116649{col 61}{space 1}   -3.12{col 70}{space 3}0.002{col 78}{space 4} .1541361{col 91}{space 3} .6522364
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.003973{col 50}{space 2} .2001391{col 61}{space 1}    0.02{col 70}{space 3}0.984{col 78}{space 4} .6791814{col 91}{space 3} 1.484084
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2}   .88363{col 50}{space 2} .1492986{col 61}{space 1}   -0.73{col 70}{space 3}0.464{col 78}{space 4}  .634464{col 91}{space 3} 1.230648
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .1342537{col 50}{space 2} .0481985{col 61}{space 1}   -5.59{col 70}{space 3}0.000{col 78}{space 4} .0664114{col 91}{space 3} .2714002
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 3: Worsen v Improve
. svy: logit AI_directionworse ib3.age_3 if infullmodel_Table2 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}   2{txt},{res}   1320{txt}){col 67}= {res}      6.21
{txt}{col 49}Prob > F{col 67}= {res}    0.0021

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}  Linearized
{col 1}AI_directionworse{col 19}{c |} Odds Ratio{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}age_3group {c |}
{space 11}18-29  {c |}{col 19}{res}{space 2}  .493023{col 31}{space 2} .1008862{col 42}{space 1}   -3.46{col 51}{space 3}0.001{col 59}{space 4} .3300106{col 72}{space 3} .7365571
{txt}{space 11}30-49  {c |}{col 19}{res}{space 2} .6727093{col 31}{space 2} .1085475{col 42}{space 1}   -2.46{col 51}{space 3}0.014{col 59}{space 4} .4901767{col 72}{space 3} .9232135
{txt}{space 17} {c |}
{space 12}_cons {c |}{col 19}{res}{space 2}  3.60276{col 31}{space 2}  .481712{col 42}{space 1}    9.59{col 51}{space 3}0.000{col 59}{space 4} 2.771532{col 72}{space 3} 4.683287
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_directionworse ib3.age_3 $controls_demographics if infullmodel_Table2 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}  17{txt},{res}   1305{txt}){col 67}= {res}      4.64
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                   AI_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}age_3group {c |}
{space 30}18-29  {c |}{col 38}{res}{space 2} .5776636{col 50}{space 2} .1231551{col 61}{space 1}   -2.57{col 70}{space 3}0.010{col 78}{space 4} .3802213{col 91}{space 3} .8776341
{txt}{space 30}30-49  {c |}{col 38}{res}{space 2} .9420499{col 50}{space 2} .1677544{col 61}{space 1}   -0.34{col 70}{space 3}0.737{col 78}{space 4} .6642907{col 91}{space 3} 1.335948
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} 1.343212{col 50}{space 2} .1961699{col 61}{space 1}    2.02{col 70}{space 3}0.044{col 78}{space 4} 1.008595{col 91}{space 3} 1.788845
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7325523{col 50}{space 2} .1120903{col 61}{space 1}   -2.03{col 70}{space 3}0.042{col 78}{space 4} .5425927{col 91}{space 3}  .989016
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 2.387622{col 50}{space 2} .5475305{col 61}{space 1}    3.80{col 70}{space 3}0.000{col 78}{space 4}  1.52261{col 91}{space 3} 3.744057
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.240947{col 50}{space 2} .2031517{col 61}{space 1}    1.32{col 70}{space 3}0.188{col 78}{space 4} .9000731{col 91}{space 3} 1.710916
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}  1.22502{col 50}{space 2} .4068413{col 61}{space 1}    0.61{col 70}{space 3}0.541{col 78}{space 4} .6385446{col 91}{space 3} 2.350146
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.053222{col 50}{space 2} .2908356{col 61}{space 1}    0.19{col 70}{space 3}0.851{col 78}{space 4} .6127091{col 91}{space 3} 1.810445
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .6364657{col 50}{space 2}  .396653{col 61}{space 1}   -0.72{col 70}{space 3}0.469{col 78}{space 4} .1874172{col 91}{space 3} 2.161426
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.921637{col 50}{space 2} .6822025{col 61}{space 1}    1.84{col 70}{space 3}0.066{col 78}{space 4} .9576527{col 91}{space 3} 3.855977
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 2.194025{col 50}{space 2} .7060782{col 61}{space 1}    2.44{col 70}{space 3}0.015{col 78}{space 4} 1.166965{col 91}{space 3} 4.125013
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.954344{col 50}{space 2} .5733307{col 61}{space 1}    2.28{col 70}{space 3}0.023{col 78}{space 4} 1.099161{col 91}{space 3} 3.474884
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.511639{col 50}{space 2} .4308267{col 61}{space 1}    1.45{col 70}{space 3}0.147{col 78}{space 4} .8642265{col 91}{space 3} 2.644042
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.146499{col 50}{space 2} .2142211{col 61}{space 1}    0.73{col 70}{space 3}0.464{col 78}{space 4} .7946625{col 91}{space 3} 1.654112
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 2.509639{col 50}{space 2} .6072188{col 61}{space 1}    3.80{col 70}{space 3}0.000{col 78}{space 4} 1.561242{col 91}{space 3} 4.034152
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.649087{col 50}{space 2} .5168764{col 61}{space 1}    1.60{col 70}{space 3}0.111{col 78}{space 4}  .891672{col 91}{space 3} 3.049874
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.619572{col 50}{space 2} .4988888{col 61}{space 1}    1.57{col 70}{space 3}0.118{col 78}{space 4} .8850298{col 91}{space 3} 2.963757
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 1.158567{col 50}{space 2} .3855995{col 61}{space 1}    0.44{col 70}{space 3}0.658{col 78}{space 4}   .60306{col 91}{space 3} 2.225777
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_directionworse ib3.age_3 $controls_demographics $controls_objective if infullmodel_Table2 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}  23{txt},{res}   1299{txt}){col 67}= {res}      4.25
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                   AI_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}age_3group {c |}
{space 30}18-29  {c |}{col 38}{res}{space 2}   .58192{col 50}{space 2} .1248418{col 61}{space 1}   -2.52{col 70}{space 3}0.012{col 78}{space 4} .3820181{col 91}{space 3} .8864264
{txt}{space 30}30-49  {c |}{col 38}{res}{space 2} .9565765{col 50}{space 2} .1712169{col 61}{space 1}   -0.25{col 70}{space 3}0.804{col 78}{space 4}  .673324{col 91}{space 3} 1.358987
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} 1.335884{col 50}{space 2} .1975336{col 61}{space 1}    1.96{col 70}{space 3}0.050{col 78}{space 4} .9995127{col 91}{space 3} 1.785456
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7356201{col 50}{space 2} .1132835{col 61}{space 1}   -1.99{col 70}{space 3}0.046{col 78}{space 4} .5438144{col 91}{space 3} .9950766
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 2.412895{col 50}{space 2} .5516282{col 61}{space 1}    3.85{col 70}{space 3}0.000{col 78}{space 4} 1.540852{col 91}{space 3} 3.778468
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.315665{col 50}{space 2} .2214155{col 61}{space 1}    1.63{col 70}{space 3}0.103{col 78}{space 4} .9457228{col 91}{space 3} 1.830319
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.318259{col 50}{space 2} .4386386{col 61}{space 1}    0.83{col 70}{space 3}0.406{col 78}{space 4}  .686296{col 91}{space 3} 2.532153
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2}  .926384{col 50}{space 2} .2584575{col 61}{space 1}   -0.27{col 70}{space 3}0.784{col 78}{space 4} .5359093{col 91}{space 3} 1.601367
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5966104{col 50}{space 2} .3877683{col 61}{space 1}   -0.79{col 70}{space 3}0.427{col 78}{space 4} .1667029{col 91}{space 3}   2.1352
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.823815{col 50}{space 2} .6373137{col 61}{space 1}    1.72{col 70}{space 3}0.086{col 78}{space 4} .9188914{col 91}{space 3} 3.619906
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 2.220856{col 50}{space 2} .7064391{col 61}{space 1}    2.51{col 70}{space 3}0.012{col 78}{space 4} 1.189901{col 91}{space 3} 4.145053
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.988575{col 50}{space 2} .5775212{col 61}{space 1}    2.37{col 70}{space 3}0.018{col 78}{space 4} 1.124889{col 91}{space 3} 3.515397
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.569265{col 50}{space 2} .4418033{col 61}{space 1}    1.60{col 70}{space 3}0.110{col 78}{space 4} .9033028{col 91}{space 3}  2.72621
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}  .926379{col 50}{space 2} .1941269{col 61}{space 1}   -0.36{col 70}{space 3}0.715{col 78}{space 4} .6141183{col 91}{space 3} 1.397415
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} .8738151{col 50}{space 2} .3061096{col 61}{space 1}   -0.39{col 70}{space 3}0.700{col 78}{space 4} .4394994{col 91}{space 3} 1.737324
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.107186{col 50}{space 2} .4118154{col 61}{space 1}    0.27{col 70}{space 3}0.784{col 78}{space 4} .5337372{col 91}{space 3} 2.296748
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .9733152{col 50}{space 2}   .39149{col 61}{space 1}   -0.07{col 70}{space 3}0.946{col 78}{space 4} .4421467{col 91}{space 3} 2.142597
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .7403207{col 50}{space 2} .1530683{col 61}{space 1}   -1.45{col 70}{space 3}0.146{col 78}{space 4} .4934744{col 91}{space 3} 1.110645
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.080125{col 50}{space 2} .4675268{col 61}{space 1}    0.18{col 70}{space 3}0.859{col 78}{space 4} .4620594{col 91}{space 3} 2.524936
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} .8527179{col 50}{space 2} .3655586{col 61}{space 1}   -0.37{col 70}{space 3}0.710{col 78}{space 4} .3677581{col 91}{space 3} 1.977191
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} 3.897365{col 50}{space 2}  1.55288{col 61}{space 1}    3.41{col 70}{space 3}0.001{col 78}{space 4} 1.783628{col 91}{space 3} 8.516043
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .9586039{col 50}{space 2} .2224665{col 61}{space 1}   -0.18{col 70}{space 3}0.855{col 78}{space 4} .6080186{col 91}{space 3} 1.511338
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.227994{col 50}{space 2} .2313644{col 61}{space 1}    1.09{col 70}{space 3}0.276{col 78}{space 4} .8485466{col 91}{space 3}  1.77712
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 1.488882{col 50}{space 2} .6466984{col 61}{space 1}    0.92{col 70}{space 3}0.360{col 78}{space 4} .6350391{col 91}{space 3} 3.490761
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. 
. 
. ******* Gender ********
. 
. * Model 1: Worsen 
. svy: logit AI_worsen ib1.female_dummy if infullmodel_Table2 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   1{txt},{res}   3962{txt}){col 67}= {res}      0.21
{txt}{col 49}Prob > F{col 67}= {res}    0.6446

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}   AI_worsen{col 14}{c |} Odds Ratio{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
female_dummy {c |}
{space 7}Male  {c |}{col 14}{res}{space 2} 1.037883{col 26}{space 2}  .083657{col 37}{space 1}    0.46{col 46}{space 3}0.645{col 54}{space 4} .8861707{col 67}{space 3} 1.215568
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .3038898{col 26}{space 2} .0161685{col 37}{space 1}  -22.39{col 46}{space 3}0.000{col 54}{space 4} .2737878{col 67}{space 3} .3373015
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_worsen ib1.female_dummy $controls_demographics if infullmodel_Table2 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      4.27
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.148347{col 50}{space 2} .1010121{col 61}{space 1}    1.57{col 70}{space 3}0.116{col 78}{space 4} .9664422{col 91}{space 3}  1.36449
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .8927627{col 50}{space 2} .1100534{col 61}{space 1}   -0.92{col 70}{space 3}0.358{col 78}{space 4} .7010902{col 91}{space 3} 1.136837
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .7758053{col 50}{space 2}  .105277{col 61}{space 1}   -1.87{col 70}{space 3}0.061{col 78}{space 4} .5945784{col 91}{space 3}  1.01227
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.282467{col 50}{space 2} .1163864{col 61}{space 1}    2.74{col 70}{space 3}0.006{col 78}{space 4} 1.073431{col 91}{space 3} 1.532209
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.279242{col 50}{space 2}  .134588{col 61}{space 1}    2.34{col 70}{space 3}0.019{col 78}{space 4}  1.04081{col 91}{space 3} 1.572296
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9047129{col 50}{space 2} .0865865{col 61}{space 1}   -1.05{col 70}{space 3}0.295{col 78}{space 4}   .74993{col 91}{space 3} 1.091442
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .8892559{col 50}{space 2} .1604124{col 61}{space 1}   -0.65{col 70}{space 3}0.515{col 78}{space 4} .6243557{col 91}{space 3} 1.266548
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9881668{col 50}{space 2} .1427731{col 61}{space 1}   -0.08{col 70}{space 3}0.934{col 78}{space 4} .7444041{col 91}{space 3} 1.311752
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8492572{col 50}{space 2} .2455706{col 61}{space 1}   -0.57{col 70}{space 3}0.572{col 78}{space 4} .4817605{col 91}{space 3} 1.497088
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.111529{col 50}{space 2} .1852479{col 61}{space 1}    0.63{col 70}{space 3}0.526{col 78}{space 4}  .801707{col 91}{space 3} 1.541082
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .9992331{col 50}{space 2} .1627154{col 61}{space 1}   -0.00{col 70}{space 3}0.996{col 78}{space 4} .7261303{col 91}{space 3} 1.375052
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.011691{col 50}{space 2} .1605068{col 61}{space 1}    0.07{col 70}{space 3}0.942{col 78}{space 4} .7412453{col 91}{space 3}  1.38081
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.081836{col 50}{space 2} .1754481{col 61}{space 1}    0.49{col 70}{space 3}0.628{col 78}{space 4} .7871807{col 91}{space 3} 1.486785
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.059863{col 50}{space 2} .1222794{col 61}{space 1}    0.50{col 70}{space 3}0.614{col 78}{space 4} .8453065{col 91}{space 3} 1.328878
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.498164{col 50}{space 2} .1787707{col 61}{space 1}    3.39{col 70}{space 3}0.001{col 78}{space 4} 1.185652{col 91}{space 3} 1.893046
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .6336916{col 50}{space 2} .0949955{col 61}{space 1}   -3.04{col 70}{space 3}0.002{col 78}{space 4} .4723206{col 91}{space 3} .8501958
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}  .599223{col 50}{space 2} .1024432{col 61}{space 1}   -3.00{col 70}{space 3}0.003{col 78}{space 4} .4285707{col 91}{space 3} .8378272
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2915808{col 50}{space 2} .0557617{col 61}{space 1}   -6.44{col 70}{space 3}0.000{col 78}{space 4}  .200413{col 91}{space 3} .4242208
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_worsen ib1.female_dummy $controls_demographics  $controls_objective if infullmodel_Table2 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      4.36
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.139543{col 50}{space 2} .1011743{col 61}{space 1}    1.47{col 70}{space 3}0.141{col 78}{space 4} .9574888{col 91}{space 3} 1.356213
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .9121425{col 50}{space 2}  .111979{col 61}{space 1}   -0.75{col 70}{space 3}0.454{col 78}{space 4} .7170231{col 91}{space 3} 1.160359
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .7963834{col 50}{space 2}  .107791{col 61}{space 1}   -1.68{col 70}{space 3}0.093{col 78}{space 4} .6107681{col 91}{space 3} 1.038408
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  1.26827{col 50}{space 2}  .116173{col 61}{space 1}    2.59{col 70}{space 3}0.010{col 78}{space 4} 1.059786{col 91}{space 3} 1.517768
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.270618{col 50}{space 2} .1350353{col 61}{space 1}    2.25{col 70}{space 3}0.024{col 78}{space 4} 1.031634{col 91}{space 3} 1.564964
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9688247{col 50}{space 2} .0945639{col 61}{space 1}   -0.32{col 70}{space 3}0.746{col 78}{space 4} .8000861{col 91}{space 3}  1.17315
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9331544{col 50}{space 2} .1706257{col 61}{space 1}   -0.38{col 70}{space 3}0.705{col 78}{space 4} .6520264{col 91}{space 3} 1.335494
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9994992{col 50}{space 2} .1454519{col 61}{space 1}   -0.00{col 70}{space 3}0.997{col 78}{space 4} .7514043{col 91}{space 3} 1.329509
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8543309{col 50}{space 2} .2446505{col 61}{space 1}   -0.55{col 70}{space 3}0.583{col 78}{space 4} .4873009{col 91}{space 3} 1.497804
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}   1.0992{col 50}{space 2} .1850128{col 61}{space 1}    0.56{col 70}{space 3}0.574{col 78}{space 4} .7902459{col 91}{space 3} 1.528943
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.003071{col 50}{space 2} .1648735{col 61}{space 1}    0.02{col 70}{space 3}0.985{col 78}{space 4}  .726738{col 91}{space 3} 1.384476
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.023778{col 50}{space 2} .1646043{col 61}{space 1}    0.15{col 70}{space 3}0.884{col 78}{space 4} .7469762{col 91}{space 3} 1.403153
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.060449{col 50}{space 2} .1731385{col 61}{space 1}    0.36{col 70}{space 3}0.719{col 78}{space 4} .7699674{col 91}{space 3} 1.460519
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}   .94564{col 50}{space 2} .1172553{col 61}{space 1}   -0.45{col 70}{space 3}0.652{col 78}{space 4} .7415634{col 91}{space 3} 1.205878
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.000113{col 50}{space 2} .1883523{col 61}{space 1}    0.00{col 70}{space 3}1.000{col 78}{space 4} .6913415{col 91}{space 3} 1.446791
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .7392922{col 50}{space 2} .1278909{col 61}{space 1}   -1.75{col 70}{space 3}0.081{col 78}{space 4} .5266486{col 91}{space 3} 1.037794
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .8875048{col 50}{space 2} .2078441{col 61}{space 1}   -0.51{col 70}{space 3}0.610{col 78}{space 4} .5607477{col 91}{space 3} 1.404669
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.132371{col 50}{space 2} .1272744{col 61}{space 1}    1.11{col 70}{space 3}0.269{col 78}{space 4} .9084218{col 91}{space 3} 1.411529
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.293345{col 50}{space 2} .3083182{col 61}{space 1}    1.08{col 70}{space 3}0.281{col 78}{space 4} .8104681{col 91}{space 3}  2.06392
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.012834{col 50}{space 2} .2390897{col 61}{space 1}    0.05{col 70}{space 3}0.957{col 78}{space 4} .6375913{col 91}{space 3}  1.60892
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2}  1.47375{col 50}{space 2} .3200393{col 61}{space 1}    1.79{col 70}{space 3}0.074{col 78}{space 4} .9627655{col 91}{space 3} 2.255938
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .8816561{col 50}{space 2}   .12698{col 61}{space 1}   -0.87{col 70}{space 3}0.382{col 78}{space 4} .6647648{col 91}{space 3} 1.169312
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.180469{col 50}{space 2} .1459234{col 61}{space 1}    1.34{col 70}{space 3}0.180{col 78}{space 4} .9264062{col 91}{space 3} 1.504208
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2133833{col 50}{space 2} .0551564{col 61}{space 1}   -5.98{col 70}{space 3}0.000{col 78}{space 4} .1285495{col 91}{space 3} .3542015
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 2: Improve 
. svy: logit AI_improve ib1.female_dummy if infullmodel_Table2 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   1{txt},{res}   3962{txt}){col 67}= {res}     24.03
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}  AI_improve{col 14}{c |} Odds Ratio{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
female_dummy {c |}
{space 7}Male  {c |}{col 14}{res}{space 2} 1.807181{col 26}{space 2} .2181798{col 37}{space 1}    4.90{col 46}{space 3}0.000{col 54}{space 4} 1.426281{col 67}{space 3} 2.289802
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0742909{col 26}{space 2} .0067072{col 37}{space 1}  -28.80{col 46}{space 3}0.000{col 54}{space 4} .0622391{col 67}{space 3} .0886765
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_improve ib1.female_dummy $controls_demographics if infullmodel_Table2 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      9.00
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                          AI_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.593788{col 50}{space 2} .2071881{col 61}{space 1}    3.59{col 70}{space 3}0.000{col 78}{space 4} 1.235216{col 91}{space 3} 2.056451
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .5859237{col 50}{space 2} .0967461{col 61}{space 1}   -3.24{col 70}{space 3}0.001{col 78}{space 4} .4238869{col 91}{space 3} .8099013
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .4313816{col 50}{space 2} .0848871{col 61}{space 1}   -4.27{col 70}{space 3}0.000{col 78}{space 4} .2932994{col 91}{space 3} .6344712
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.753261{col 50}{space 2} .2386836{col 61}{space 1}    4.12{col 70}{space 3}0.000{col 78}{space 4} 1.342552{col 91}{space 3} 2.289613
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .4766213{col 50}{space 2} .0975186{col 61}{space 1}   -3.62{col 70}{space 3}0.000{col 78}{space 4} .3191252{col 91}{space 3} .7118456
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2}  .703992{col 50}{space 2} .1022251{col 61}{space 1}   -2.42{col 70}{space 3}0.016{col 78}{space 4} .5295767{col 91}{space 3} .9358507
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .6980172{col 50}{space 2} .1996649{col 61}{space 1}   -1.26{col 70}{space 3}0.209{col 78}{space 4} .3983906{col 91}{space 3} 1.222991
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.045743{col 50}{space 2} .2381017{col 61}{space 1}    0.20{col 70}{space 3}0.844{col 78}{space 4} .6692037{col 91}{space 3} 1.634148
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.253748{col 50}{space 2} .6701505{col 61}{space 1}    0.42{col 70}{space 3}0.672{col 78}{space 4} .4396317{col 91}{space 3} 3.575457
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5114603{col 50}{space 2} .1552909{col 61}{space 1}   -2.21{col 70}{space 3}0.027{col 78}{space 4} .2820259{col 91}{space 3} .9275449
{txt}{space 34}3  {c |}{col 38}{res}{space 2}  .431203{col 50}{space 2} .1200384{col 61}{space 1}   -3.02{col 70}{space 3}0.003{col 78}{space 4} .2498341{col 91}{space 3} .7442382
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .4891384{col 50}{space 2} .1216417{col 61}{space 1}   -2.88{col 70}{space 3}0.004{col 78}{space 4}   .30039{col 91}{space 3} .7964859
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .6643646{col 50}{space 2} .1598227{col 61}{space 1}   -1.70{col 70}{space 3}0.089{col 78}{space 4} .4145481{col 91}{space 3} 1.064727
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .8602597{col 50}{space 2} .1431378{col 61}{space 1}   -0.90{col 70}{space 3}0.366{col 78}{space 4} .6208056{col 91}{space 3} 1.192075
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}  .483637{col 50}{space 2} .1091257{col 61}{space 1}   -3.22{col 70}{space 3}0.001{col 78}{space 4} .3107417{col 91}{space 3} .7527305
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .4243676{col 50}{space 2} .1168096{col 61}{space 1}   -3.11{col 70}{space 3}0.002{col 78}{space 4} .2473845{col 91}{space 3} .7279674
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .3840638{col 50}{space 2} .1048289{col 61}{space 1}   -3.51{col 70}{space 3}0.000{col 78}{space 4} .2249053{col 91}{space 3} .6558537
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2}  .296137{col 50}{space 2} .0943506{col 61}{space 1}   -3.82{col 70}{space 3}0.000{col 78}{space 4} .1585671{col 91}{space 3}   .55306
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_improve ib1.female_dummy $controls_demographics $controls_objective if infullmodel_Table2 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      7.85
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                          AI_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.582859{col 50}{space 2} .2089079{col 61}{space 1}    3.48{col 70}{space 3}0.001{col 78}{space 4} 1.221983{col 91}{space 3} 2.050309
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .5967726{col 50}{space 2} .0987241{col 61}{space 1}   -3.12{col 70}{space 3}0.002{col 78}{space 4}  .431471{col 91}{space 3} .8254034
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .4389943{col 50}{space 2} .0868928{col 61}{space 1}   -4.16{col 70}{space 3}0.000{col 78}{space 4} .2977995{col 91}{space 3} .6471334
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.732196{col 50}{space 2} .2368375{col 61}{space 1}    4.02{col 70}{space 3}0.000{col 78}{space 4} 1.324889{col 91}{space 3} 2.264721
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .4842575{col 50}{space 2} .0992227{col 61}{space 1}   -3.54{col 70}{space 3}0.000{col 78}{space 4} .3240521{col 91}{space 3} .7236656
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .6974604{col 50}{space 2} .1028677{col 61}{space 1}   -2.44{col 70}{space 3}0.015{col 78}{space 4} .5223221{col 91}{space 3} .9313239
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .6836831{col 50}{space 2} .1929683{col 61}{space 1}   -1.35{col 70}{space 3}0.178{col 78}{space 4} .3931256{col 91}{space 3}  1.18899
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.129698{col 50}{space 2} .2598135{col 61}{space 1}    0.53{col 70}{space 3}0.596{col 78}{space 4} .7196789{col 91}{space 3} 1.773314
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.339877{col 50}{space 2} .7204718{col 61}{space 1}    0.54{col 70}{space 3}0.586{col 78}{space 4} .4668973{col 91}{space 3} 3.845108
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5083028{col 50}{space 2} .1523285{col 61}{space 1}   -2.26{col 70}{space 3}0.024{col 78}{space 4} .2824594{col 91}{space 3}  .914722
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .4113217{col 50}{space 2} .1135379{col 61}{space 1}   -3.22{col 70}{space 3}0.001{col 78}{space 4} .2394149{col 91}{space 3} .7066626
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .4648291{col 50}{space 2} .1149079{col 61}{space 1}   -3.10{col 70}{space 3}0.002{col 78}{space 4} .2862912{col 91}{space 3} .7547074
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .6062726{col 50}{space 2} .1449004{col 61}{space 1}   -2.09{col 70}{space 3}0.036{col 78}{space 4} .3794607{col 91}{space 3} .9686549
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.010877{col 50}{space 2} .1838329{col 61}{space 1}    0.06{col 70}{space 3}0.953{col 78}{space 4} .7077112{col 91}{space 3} 1.443913
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.090424{col 50}{space 2} .3315547{col 61}{space 1}    0.28{col 70}{space 3}0.776{col 78}{space 4} .6007582{col 91}{space 3} 1.979207
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .7050556{col 50}{space 2} .2294216{col 61}{space 1}   -1.07{col 70}{space 3}0.283{col 78}{space 4} .3725306{col 91}{space 3} 1.334396
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}  .859264{col 50}{space 2} .2912932{col 61}{space 1}   -0.45{col 70}{space 3}0.655{col 78}{space 4} .4420572{col 91}{space 3} 1.670224
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.600942{col 50}{space 2} .2810704{col 61}{space 1}    2.68{col 70}{space 3}0.007{col 78}{space 4} 1.134718{col 91}{space 3} 2.258723
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.005969{col 50}{space 2} .3909627{col 61}{space 1}    0.02{col 70}{space 3}0.988{col 78}{space 4} .4695373{col 91}{space 3} 2.155257
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2}  1.09556{col 50}{space 2} .4040863{col 61}{space 1}    0.25{col 70}{space 3}0.805{col 78}{space 4} .5315977{col 91}{space 3}  2.25782
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .3170697{col 50}{space 2}  .116649{col 61}{space 1}   -3.12{col 70}{space 3}0.002{col 78}{space 4} .1541361{col 91}{space 3} .6522364
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.003973{col 50}{space 2} .2001391{col 61}{space 1}    0.02{col 70}{space 3}0.984{col 78}{space 4} .6791814{col 91}{space 3} 1.484084
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2}   .88363{col 50}{space 2} .1492986{col 61}{space 1}   -0.73{col 70}{space 3}0.464{col 78}{space 4}  .634464{col 91}{space 3} 1.230648
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .1932081{col 50}{space 2} .0784027{col 61}{space 1}   -4.05{col 70}{space 3}0.000{col 78}{space 4} .0871982{col 91}{space 3} .4280981
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 3: Worsen v Improve
. svy: logit AI_directionworse ib1.female_dummy if infullmodel_Table2 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}   1{txt},{res}   1321{txt}){col 67}= {res}     14.27
{txt}{col 49}Prob > F{col 67}= {res}    0.0002

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}  Linearized
{col 1}AI_directionworse{col 19}{c |} Odds Ratio{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}female_dummy {c |}
{space 12}Male  {c |}{col 19}{res}{space 2} .6010611{col 31}{space 2} .0809869{col 42}{space 1}   -3.78{col 51}{space 3}0.000{col 59}{space 4} .4614479{col 72}{space 3} .7829151
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 3.370245{col 31}{space 2}   .33254{col 42}{space 1}   12.31{col 51}{space 3}0.000{col 59}{space 4} 2.777135{col 72}{space 3} 4.090025
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_directionworse ib1.female_dummy $controls_demographics if infullmodel_Table2 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}  17{txt},{res}   1305{txt}){col 67}= {res}      4.64
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                   AI_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} .7444839{col 50}{space 2} .1087284{col 61}{space 1}   -2.02{col 70}{space 3}0.044{col 78}{space 4} .5590199{col 91}{space 3} .9914786
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.630793{col 50}{space 2} .3050056{col 61}{space 1}    2.61{col 70}{space 3}0.009{col 78}{space 4} 1.129935{col 91}{space 3} 2.353663
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.731111{col 50}{space 2} .3690646{col 61}{space 1}    2.57{col 70}{space 3}0.010{col 78}{space 4} 1.139427{col 91}{space 3} 2.630047
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7325523{col 50}{space 2} .1120903{col 61}{space 1}   -2.03{col 70}{space 3}0.042{col 78}{space 4} .5425927{col 91}{space 3}  .989016
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 2.387622{col 50}{space 2} .5475305{col 61}{space 1}    3.80{col 70}{space 3}0.000{col 78}{space 4}  1.52261{col 91}{space 3} 3.744057
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.240947{col 50}{space 2} .2031517{col 61}{space 1}    1.32{col 70}{space 3}0.188{col 78}{space 4} .9000731{col 91}{space 3} 1.710916
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}  1.22502{col 50}{space 2} .4068413{col 61}{space 1}    0.61{col 70}{space 3}0.541{col 78}{space 4} .6385446{col 91}{space 3} 2.350146
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.053222{col 50}{space 2} .2908356{col 61}{space 1}    0.19{col 70}{space 3}0.851{col 78}{space 4} .6127091{col 91}{space 3} 1.810445
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .6364657{col 50}{space 2}  .396653{col 61}{space 1}   -0.72{col 70}{space 3}0.469{col 78}{space 4} .1874172{col 91}{space 3} 2.161426
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.921637{col 50}{space 2} .6822025{col 61}{space 1}    1.84{col 70}{space 3}0.066{col 78}{space 4} .9576527{col 91}{space 3} 3.855977
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 2.194025{col 50}{space 2} .7060782{col 61}{space 1}    2.44{col 70}{space 3}0.015{col 78}{space 4} 1.166965{col 91}{space 3} 4.125013
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.954344{col 50}{space 2} .5733307{col 61}{space 1}    2.28{col 70}{space 3}0.023{col 78}{space 4} 1.099161{col 91}{space 3} 3.474884
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.511639{col 50}{space 2} .4308267{col 61}{space 1}    1.45{col 70}{space 3}0.147{col 78}{space 4} .8642265{col 91}{space 3} 2.644042
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.146499{col 50}{space 2} .2142211{col 61}{space 1}    0.73{col 70}{space 3}0.464{col 78}{space 4} .7946625{col 91}{space 3} 1.654112
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 2.509639{col 50}{space 2} .6072188{col 61}{space 1}    3.80{col 70}{space 3}0.000{col 78}{space 4} 1.561242{col 91}{space 3} 4.034152
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.649087{col 50}{space 2} .5168764{col 61}{space 1}    1.60{col 70}{space 3}0.111{col 78}{space 4}  .891672{col 91}{space 3} 3.049874
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.619572{col 50}{space 2} .4988888{col 61}{space 1}    1.57{col 70}{space 3}0.118{col 78}{space 4} .8850298{col 91}{space 3} 2.963757
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .8989608{col 50}{space 2}  .320796{col 61}{space 1}   -0.30{col 70}{space 3}0.765{col 78}{space 4} .4463846{col 91}{space 3} 1.810391
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_directionworse ib1.female_dummy $controls_demographics $controls_objective if infullmodel_Table2 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}  23{txt},{res}   1299{txt}){col 67}= {res}      4.25
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                   AI_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} .7485681{col 50}{space 2} .1106888{col 61}{space 1}   -1.96{col 70}{space 3}0.050{col 78}{space 4} .5600811{col 91}{space 3} 1.000488
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.643828{col 50}{space 2} .3076717{col 61}{space 1}    2.66{col 70}{space 3}0.008{col 78}{space 4} 1.138657{col 91}{space 3} 2.373122
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.718449{col 50}{space 2} .3686662{col 61}{space 1}    2.52{col 70}{space 3}0.012{col 78}{space 4} 1.128125{col 91}{space 3} 2.617677
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7356201{col 50}{space 2} .1132835{col 61}{space 1}   -1.99{col 70}{space 3}0.046{col 78}{space 4} .5438144{col 91}{space 3} .9950766
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 2.412895{col 50}{space 2} .5516282{col 61}{space 1}    3.85{col 70}{space 3}0.000{col 78}{space 4} 1.540852{col 91}{space 3} 3.778468
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.315665{col 50}{space 2} .2214155{col 61}{space 1}    1.63{col 70}{space 3}0.103{col 78}{space 4} .9457228{col 91}{space 3} 1.830319
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.318259{col 50}{space 2} .4386386{col 61}{space 1}    0.83{col 70}{space 3}0.406{col 78}{space 4}  .686296{col 91}{space 3} 2.532153
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2}  .926384{col 50}{space 2} .2584575{col 61}{space 1}   -0.27{col 70}{space 3}0.784{col 78}{space 4} .5359093{col 91}{space 3} 1.601367
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5966104{col 50}{space 2} .3877683{col 61}{space 1}   -0.79{col 70}{space 3}0.427{col 78}{space 4} .1667029{col 91}{space 3}   2.1352
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.823815{col 50}{space 2} .6373137{col 61}{space 1}    1.72{col 70}{space 3}0.086{col 78}{space 4} .9188914{col 91}{space 3} 3.619906
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 2.220856{col 50}{space 2} .7064391{col 61}{space 1}    2.51{col 70}{space 3}0.012{col 78}{space 4} 1.189901{col 91}{space 3} 4.145053
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.988575{col 50}{space 2} .5775212{col 61}{space 1}    2.37{col 70}{space 3}0.018{col 78}{space 4} 1.124889{col 91}{space 3} 3.515397
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.569265{col 50}{space 2} .4418033{col 61}{space 1}    1.60{col 70}{space 3}0.110{col 78}{space 4} .9033028{col 91}{space 3}  2.72621
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}  .926379{col 50}{space 2} .1941269{col 61}{space 1}   -0.36{col 70}{space 3}0.715{col 78}{space 4} .6141183{col 91}{space 3} 1.397415
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} .8738151{col 50}{space 2} .3061096{col 61}{space 1}   -0.39{col 70}{space 3}0.700{col 78}{space 4} .4394994{col 91}{space 3} 1.737324
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.107186{col 50}{space 2} .4118154{col 61}{space 1}    0.27{col 70}{space 3}0.784{col 78}{space 4} .5337372{col 91}{space 3} 2.296748
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .9733152{col 50}{space 2}   .39149{col 61}{space 1}   -0.07{col 70}{space 3}0.946{col 78}{space 4} .4421467{col 91}{space 3} 2.142597
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .7403207{col 50}{space 2} .1530683{col 61}{space 1}   -1.45{col 70}{space 3}0.146{col 78}{space 4} .4934744{col 91}{space 3} 1.110645
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.080125{col 50}{space 2} .4675268{col 61}{space 1}    0.18{col 70}{space 3}0.859{col 78}{space 4} .4620594{col 91}{space 3} 2.524936
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} .8527179{col 50}{space 2} .3655586{col 61}{space 1}   -0.37{col 70}{space 3}0.710{col 78}{space 4} .3677581{col 91}{space 3} 1.977191
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} 3.897365{col 50}{space 2}  1.55288{col 61}{space 1}    3.41{col 70}{space 3}0.001{col 78}{space 4} 1.783628{col 91}{space 3} 8.516043
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .9586039{col 50}{space 2} .2224665{col 61}{space 1}   -0.18{col 70}{space 3}0.855{col 78}{space 4} .6080186{col 91}{space 3} 1.511338
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.227994{col 50}{space 2} .2313644{col 61}{space 1}    1.09{col 70}{space 3}0.276{col 78}{space 4} .8485466{col 91}{space 3}  1.77712
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 1.157423{col 50}{space 2} .5413573{col 61}{space 1}    0.31{col 70}{space 3}0.755{col 78}{space 4} .4623788{col 91}{space 3} 2.897255
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. 
. ******* Education ********
. 
. * Model 1: Worsen 
. svy: logit AI_worsen i.degree_dummy if infullmodel_Table2 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   1{txt},{res}   3962{txt}){col 67}= {res}     17.91
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}  Linearized
{col 1}     AI_worsen{col 16}{c |} Odds Ratio{col 28}   Std. Err.{col 40}      t{col 48}   P>|t|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}degree_dummy {c |}
Degree-Holder  {c |}{col 16}{res}{space 2} 1.408135{col 28}{space 2} .1138872{col 39}{space 1}    4.23{col 48}{space 3}0.000{col 56}{space 4} 1.201655{col 69}{space 3} 1.650094
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .2632645{col 28}{space 2} .0151077{col 39}{space 1}  -23.26{col 48}{space 3}0.000{col 56}{space 4} .2352505{col 69}{space 3} .2946146
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_worsen i.degree_dummy $controls_demographics if infullmodel_Table2 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      4.27
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.282467{col 50}{space 2} .1163864{col 61}{space 1}    2.74{col 70}{space 3}0.006{col 78}{space 4} 1.073431{col 91}{space 3} 1.532209
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .8927627{col 50}{space 2} .1100534{col 61}{space 1}   -0.92{col 70}{space 3}0.358{col 78}{space 4} .7010902{col 91}{space 3} 1.136837
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .7758053{col 50}{space 2}  .105277{col 61}{space 1}   -1.87{col 70}{space 3}0.061{col 78}{space 4} .5945784{col 91}{space 3}  1.01227
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}  .870817{col 50}{space 2} .0765998{col 61}{space 1}   -1.57{col 70}{space 3}0.116{col 78}{space 4} .7328747{col 91}{space 3} 1.034723
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.279242{col 50}{space 2}  .134588{col 61}{space 1}    2.34{col 70}{space 3}0.019{col 78}{space 4}  1.04081{col 91}{space 3} 1.572296
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9047129{col 50}{space 2} .0865865{col 61}{space 1}   -1.05{col 70}{space 3}0.295{col 78}{space 4}   .74993{col 91}{space 3} 1.091442
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .8892559{col 50}{space 2} .1604124{col 61}{space 1}   -0.65{col 70}{space 3}0.515{col 78}{space 4} .6243557{col 91}{space 3} 1.266548
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9881668{col 50}{space 2} .1427731{col 61}{space 1}   -0.08{col 70}{space 3}0.934{col 78}{space 4} .7444041{col 91}{space 3} 1.311752
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8492572{col 50}{space 2} .2455706{col 61}{space 1}   -0.57{col 70}{space 3}0.572{col 78}{space 4} .4817605{col 91}{space 3} 1.497088
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.111529{col 50}{space 2} .1852479{col 61}{space 1}    0.63{col 70}{space 3}0.526{col 78}{space 4}  .801707{col 91}{space 3} 1.541082
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .9992331{col 50}{space 2} .1627154{col 61}{space 1}   -0.00{col 70}{space 3}0.996{col 78}{space 4} .7261303{col 91}{space 3} 1.375052
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.011691{col 50}{space 2} .1605068{col 61}{space 1}    0.07{col 70}{space 3}0.942{col 78}{space 4} .7412453{col 91}{space 3}  1.38081
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.081836{col 50}{space 2} .1754481{col 61}{space 1}    0.49{col 70}{space 3}0.628{col 78}{space 4} .7871807{col 91}{space 3} 1.486785
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.059863{col 50}{space 2} .1222794{col 61}{space 1}    0.50{col 70}{space 3}0.614{col 78}{space 4} .8453065{col 91}{space 3} 1.328878
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.498164{col 50}{space 2} .1787707{col 61}{space 1}    3.39{col 70}{space 3}0.001{col 78}{space 4} 1.185652{col 91}{space 3} 1.893046
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .6336916{col 50}{space 2} .0949955{col 61}{space 1}   -3.04{col 70}{space 3}0.002{col 78}{space 4} .4723206{col 91}{space 3} .8501958
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}  .599223{col 50}{space 2} .1024432{col 61}{space 1}   -3.00{col 70}{space 3}0.003{col 78}{space 4} .4285707{col 91}{space 3} .8378272
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .3348359{col 50}{space 2} .0651311{col 61}{space 1}   -5.62{col 70}{space 3}0.000{col 78}{space 4} .2286697{col 91}{space 3} .4902927
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_worsen i.degree_dummy $controls_demographics  $controls_objective if infullmodel_Table2 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      4.36
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  1.26827{col 50}{space 2}  .116173{col 61}{space 1}    2.59{col 70}{space 3}0.010{col 78}{space 4} 1.059786{col 91}{space 3} 1.517768
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .9121425{col 50}{space 2}  .111979{col 61}{space 1}   -0.75{col 70}{space 3}0.454{col 78}{space 4} .7170231{col 91}{space 3} 1.160359
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .7963834{col 50}{space 2}  .107791{col 61}{space 1}   -1.68{col 70}{space 3}0.093{col 78}{space 4} .6107681{col 91}{space 3} 1.038408
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .8775447{col 50}{space 2} .0779128{col 61}{space 1}   -1.47{col 70}{space 3}0.141{col 78}{space 4} .7373476{col 91}{space 3} 1.044399
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.270618{col 50}{space 2} .1350353{col 61}{space 1}    2.25{col 70}{space 3}0.024{col 78}{space 4} 1.031634{col 91}{space 3} 1.564964
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9688247{col 50}{space 2} .0945639{col 61}{space 1}   -0.32{col 70}{space 3}0.746{col 78}{space 4} .8000861{col 91}{space 3}  1.17315
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9331544{col 50}{space 2} .1706257{col 61}{space 1}   -0.38{col 70}{space 3}0.705{col 78}{space 4} .6520264{col 91}{space 3} 1.335494
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9994992{col 50}{space 2} .1454519{col 61}{space 1}   -0.00{col 70}{space 3}0.997{col 78}{space 4} .7514043{col 91}{space 3} 1.329509
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8543309{col 50}{space 2} .2446505{col 61}{space 1}   -0.55{col 70}{space 3}0.583{col 78}{space 4} .4873009{col 91}{space 3} 1.497804
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}   1.0992{col 50}{space 2} .1850128{col 61}{space 1}    0.56{col 70}{space 3}0.574{col 78}{space 4} .7902459{col 91}{space 3} 1.528943
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.003071{col 50}{space 2} .1648735{col 61}{space 1}    0.02{col 70}{space 3}0.985{col 78}{space 4}  .726738{col 91}{space 3} 1.384476
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.023778{col 50}{space 2} .1646043{col 61}{space 1}    0.15{col 70}{space 3}0.884{col 78}{space 4} .7469762{col 91}{space 3} 1.403153
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.060449{col 50}{space 2} .1731385{col 61}{space 1}    0.36{col 70}{space 3}0.719{col 78}{space 4} .7699674{col 91}{space 3} 1.460519
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}   .94564{col 50}{space 2} .1172553{col 61}{space 1}   -0.45{col 70}{space 3}0.652{col 78}{space 4} .7415634{col 91}{space 3} 1.205878
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.000113{col 50}{space 2} .1883523{col 61}{space 1}    0.00{col 70}{space 3}1.000{col 78}{space 4} .6913415{col 91}{space 3} 1.446791
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .7392922{col 50}{space 2} .1278909{col 61}{space 1}   -1.75{col 70}{space 3}0.081{col 78}{space 4} .5266486{col 91}{space 3} 1.037794
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .8875048{col 50}{space 2} .2078441{col 61}{space 1}   -0.51{col 70}{space 3}0.610{col 78}{space 4} .5607477{col 91}{space 3} 1.404669
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.132371{col 50}{space 2} .1272744{col 61}{space 1}    1.11{col 70}{space 3}0.269{col 78}{space 4} .9084218{col 91}{space 3} 1.411529
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.293345{col 50}{space 2} .3083182{col 61}{space 1}    1.08{col 70}{space 3}0.281{col 78}{space 4} .8104681{col 91}{space 3}  2.06392
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.012834{col 50}{space 2} .2390897{col 61}{space 1}    0.05{col 70}{space 3}0.957{col 78}{space 4} .6375913{col 91}{space 3}  1.60892
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2}  1.47375{col 50}{space 2} .3200393{col 61}{space 1}    1.79{col 70}{space 3}0.074{col 78}{space 4} .9627655{col 91}{space 3} 2.255938
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .8816561{col 50}{space 2}   .12698{col 61}{space 1}   -0.87{col 70}{space 3}0.382{col 78}{space 4} .6647648{col 91}{space 3} 1.169312
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.180469{col 50}{space 2} .1459234{col 61}{space 1}    1.34{col 70}{space 3}0.180{col 78}{space 4} .9264062{col 91}{space 3} 1.504208
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2431594{col 50}{space 2} .0630194{col 61}{space 1}   -5.46{col 70}{space 3}0.000{col 78}{space 4} .1462913{col 91}{space 3} .4041697
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 2: Improve 
. svy: logit AI_improve i.degree_dummy if infullmodel_Table2 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   1{txt},{res}   3962{txt}){col 67}= {res}     46.55
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}  Linearized
{col 1}    AI_improve{col 16}{c |} Odds Ratio{col 28}   Std. Err.{col 40}      t{col 48}   P>|t|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}degree_dummy {c |}
Degree-Holder  {c |}{col 16}{res}{space 2} 2.407881{col 28}{space 2} .3101353{col 39}{space 1}    6.82{col 48}{space 3}0.000{col 56}{space 4} 1.870539{col 69}{space 3} 3.099584
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .0643933{col 28}{space 2} .0067947{col 39}{space 1}  -25.99{col 48}{space 3}0.000{col 56}{space 4} .0523595{col 69}{space 3}  .079193
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_improve i.degree_dummy $controls_demographics if infullmodel_Table2 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      9.00
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                          AI_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.753261{col 50}{space 2} .2386836{col 61}{space 1}    4.12{col 70}{space 3}0.000{col 78}{space 4} 1.342552{col 91}{space 3} 2.289613
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .5859237{col 50}{space 2} .0967461{col 61}{space 1}   -3.24{col 70}{space 3}0.001{col 78}{space 4} .4238869{col 91}{space 3} .8099013
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .4313816{col 50}{space 2} .0848871{col 61}{space 1}   -4.27{col 70}{space 3}0.000{col 78}{space 4} .2932994{col 91}{space 3} .6344712
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6274358{col 50}{space 2} .0815649{col 61}{space 1}   -3.59{col 70}{space 3}0.000{col 78}{space 4} .4862747{col 91}{space 3} .8095748
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .4766213{col 50}{space 2} .0975186{col 61}{space 1}   -3.62{col 70}{space 3}0.000{col 78}{space 4} .3191252{col 91}{space 3} .7118456
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2}  .703992{col 50}{space 2} .1022251{col 61}{space 1}   -2.42{col 70}{space 3}0.016{col 78}{space 4} .5295767{col 91}{space 3} .9358507
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .6980172{col 50}{space 2} .1996649{col 61}{space 1}   -1.26{col 70}{space 3}0.209{col 78}{space 4} .3983906{col 91}{space 3} 1.222991
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.045743{col 50}{space 2} .2381017{col 61}{space 1}    0.20{col 70}{space 3}0.844{col 78}{space 4} .6692037{col 91}{space 3} 1.634148
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.253748{col 50}{space 2} .6701505{col 61}{space 1}    0.42{col 70}{space 3}0.672{col 78}{space 4} .4396317{col 91}{space 3} 3.575457
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5114603{col 50}{space 2} .1552909{col 61}{space 1}   -2.21{col 70}{space 3}0.027{col 78}{space 4} .2820259{col 91}{space 3} .9275449
{txt}{space 34}3  {c |}{col 38}{res}{space 2}  .431203{col 50}{space 2} .1200384{col 61}{space 1}   -3.02{col 70}{space 3}0.003{col 78}{space 4} .2498341{col 91}{space 3} .7442382
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .4891384{col 50}{space 2} .1216417{col 61}{space 1}   -2.88{col 70}{space 3}0.004{col 78}{space 4}   .30039{col 91}{space 3} .7964859
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .6643646{col 50}{space 2} .1598227{col 61}{space 1}   -1.70{col 70}{space 3}0.089{col 78}{space 4} .4145481{col 91}{space 3} 1.064727
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .8602597{col 50}{space 2} .1431378{col 61}{space 1}   -0.90{col 70}{space 3}0.366{col 78}{space 4} .6208056{col 91}{space 3} 1.192075
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}  .483637{col 50}{space 2} .1091257{col 61}{space 1}   -3.22{col 70}{space 3}0.001{col 78}{space 4} .3107417{col 91}{space 3} .7527305
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .4243676{col 50}{space 2} .1168096{col 61}{space 1}   -3.11{col 70}{space 3}0.002{col 78}{space 4} .2473845{col 91}{space 3} .7279674
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .3840638{col 50}{space 2} .1048289{col 61}{space 1}   -3.51{col 70}{space 3}0.000{col 78}{space 4} .2249053{col 91}{space 3} .6558537
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .4719797{col 50}{space 2} .1441441{col 61}{space 1}   -2.46{col 70}{space 3}0.014{col 78}{space 4} .2593487{col 91}{space 3} .8589393
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_improve i.degree_dummy $controls_demographics $controls_objective if infullmodel_Table2 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      7.85
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                          AI_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.732196{col 50}{space 2} .2368375{col 61}{space 1}    4.02{col 70}{space 3}0.000{col 78}{space 4} 1.324889{col 91}{space 3} 2.264721
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .5967726{col 50}{space 2} .0987241{col 61}{space 1}   -3.12{col 70}{space 3}0.002{col 78}{space 4}  .431471{col 91}{space 3} .8254034
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .4389943{col 50}{space 2} .0868928{col 61}{space 1}   -4.16{col 70}{space 3}0.000{col 78}{space 4} .2977995{col 91}{space 3} .6471334
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6317683{col 50}{space 2} .0833817{col 61}{space 1}   -3.48{col 70}{space 3}0.001{col 78}{space 4} .4877314{col 91}{space 3} .8183422
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .4842575{col 50}{space 2} .0992227{col 61}{space 1}   -3.54{col 70}{space 3}0.000{col 78}{space 4} .3240521{col 91}{space 3} .7236656
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .6974604{col 50}{space 2} .1028677{col 61}{space 1}   -2.44{col 70}{space 3}0.015{col 78}{space 4} .5223221{col 91}{space 3} .9313239
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .6836831{col 50}{space 2} .1929683{col 61}{space 1}   -1.35{col 70}{space 3}0.178{col 78}{space 4} .3931256{col 91}{space 3}  1.18899
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.129698{col 50}{space 2} .2598135{col 61}{space 1}    0.53{col 70}{space 3}0.596{col 78}{space 4} .7196789{col 91}{space 3} 1.773314
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.339877{col 50}{space 2} .7204718{col 61}{space 1}    0.54{col 70}{space 3}0.586{col 78}{space 4} .4668973{col 91}{space 3} 3.845108
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5083028{col 50}{space 2} .1523285{col 61}{space 1}   -2.26{col 70}{space 3}0.024{col 78}{space 4} .2824594{col 91}{space 3}  .914722
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .4113217{col 50}{space 2} .1135379{col 61}{space 1}   -3.22{col 70}{space 3}0.001{col 78}{space 4} .2394149{col 91}{space 3} .7066626
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .4648291{col 50}{space 2} .1149079{col 61}{space 1}   -3.10{col 70}{space 3}0.002{col 78}{space 4} .2862912{col 91}{space 3} .7547074
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .6062726{col 50}{space 2} .1449004{col 61}{space 1}   -2.09{col 70}{space 3}0.036{col 78}{space 4} .3794607{col 91}{space 3} .9686549
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.010877{col 50}{space 2} .1838329{col 61}{space 1}    0.06{col 70}{space 3}0.953{col 78}{space 4} .7077112{col 91}{space 3} 1.443913
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.090424{col 50}{space 2} .3315547{col 61}{space 1}    0.28{col 70}{space 3}0.776{col 78}{space 4} .6007582{col 91}{space 3} 1.979207
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .7050556{col 50}{space 2} .2294216{col 61}{space 1}   -1.07{col 70}{space 3}0.283{col 78}{space 4} .3725306{col 91}{space 3} 1.334396
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}  .859264{col 50}{space 2} .2912932{col 61}{space 1}   -0.45{col 70}{space 3}0.655{col 78}{space 4} .4420572{col 91}{space 3} 1.670224
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.600942{col 50}{space 2} .2810704{col 61}{space 1}    2.68{col 70}{space 3}0.007{col 78}{space 4} 1.134718{col 91}{space 3} 2.258723
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.005969{col 50}{space 2} .3909627{col 61}{space 1}    0.02{col 70}{space 3}0.988{col 78}{space 4} .4695373{col 91}{space 3} 2.155257
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2}  1.09556{col 50}{space 2} .4040863{col 61}{space 1}    0.25{col 70}{space 3}0.805{col 78}{space 4} .5315977{col 91}{space 3}  2.25782
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .3170697{col 50}{space 2}  .116649{col 61}{space 1}   -3.12{col 70}{space 3}0.002{col 78}{space 4} .1541361{col 91}{space 3} .6522364
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.003973{col 50}{space 2} .2001391{col 61}{space 1}    0.02{col 70}{space 3}0.984{col 78}{space 4} .6791814{col 91}{space 3} 1.484084
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2}   .88363{col 50}{space 2} .1492986{col 61}{space 1}   -0.73{col 70}{space 3}0.464{col 78}{space 4}  .634464{col 91}{space 3} 1.230648
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .3058212{col 50}{space 2} .1182217{col 61}{space 1}   -3.06{col 70}{space 3}0.002{col 78}{space 4} .1433233{col 91}{space 3} .6525565
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 3: Worsen v Improve
. svy: logit AI_directionworse i.degree_dummy if infullmodel_Table2 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}   1{txt},{res}   1321{txt}){col 67}= {res}     14.25
{txt}{col 49}Prob > F{col 67}= {res}    0.0002

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}  Linearized
{col 1}AI_directionworse{col 19}{c |} Odds Ratio{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}degree_dummy {c |}
{space 3}Degree-Holder  {c |}{col 19}{res}{space 2} .5848662{col 31}{space 2}   .08311{col 42}{space 1}   -3.77{col 51}{space 3}0.000{col 59}{space 4} .4425773{col 72}{space 3} .7729013
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 3.444762{col 31}{space 2} .3945742{col 42}{space 1}   10.80{col 51}{space 3}0.000{col 59}{space 4} 2.751506{col 72}{space 3} 4.312688
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_directionworse i.degree_dummy $controls_demographics if infullmodel_Table2 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}  17{txt},{res}   1305{txt}){col 67}= {res}      4.64
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                   AI_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7325523{col 50}{space 2} .1120903{col 61}{space 1}   -2.03{col 70}{space 3}0.042{col 78}{space 4} .5425927{col 91}{space 3}  .989016
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.630793{col 50}{space 2} .3050056{col 61}{space 1}    2.61{col 70}{space 3}0.009{col 78}{space 4} 1.129935{col 91}{space 3} 2.353663
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.731111{col 50}{space 2} .3690646{col 61}{space 1}    2.57{col 70}{space 3}0.010{col 78}{space 4} 1.139427{col 91}{space 3} 2.630047
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} 1.343212{col 50}{space 2} .1961699{col 61}{space 1}    2.02{col 70}{space 3}0.044{col 78}{space 4} 1.008595{col 91}{space 3} 1.788845
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 2.387622{col 50}{space 2} .5475305{col 61}{space 1}    3.80{col 70}{space 3}0.000{col 78}{space 4}  1.52261{col 91}{space 3} 3.744057
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.240947{col 50}{space 2} .2031517{col 61}{space 1}    1.32{col 70}{space 3}0.188{col 78}{space 4} .9000731{col 91}{space 3} 1.710916
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}  1.22502{col 50}{space 2} .4068413{col 61}{space 1}    0.61{col 70}{space 3}0.541{col 78}{space 4} .6385446{col 91}{space 3} 2.350146
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.053222{col 50}{space 2} .2908356{col 61}{space 1}    0.19{col 70}{space 3}0.851{col 78}{space 4} .6127091{col 91}{space 3} 1.810445
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .6364657{col 50}{space 2}  .396653{col 61}{space 1}   -0.72{col 70}{space 3}0.469{col 78}{space 4} .1874172{col 91}{space 3} 2.161426
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.921637{col 50}{space 2} .6822025{col 61}{space 1}    1.84{col 70}{space 3}0.066{col 78}{space 4} .9576527{col 91}{space 3} 3.855977
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 2.194025{col 50}{space 2} .7060782{col 61}{space 1}    2.44{col 70}{space 3}0.015{col 78}{space 4} 1.166965{col 91}{space 3} 4.125013
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.954344{col 50}{space 2} .5733307{col 61}{space 1}    2.28{col 70}{space 3}0.023{col 78}{space 4} 1.099161{col 91}{space 3} 3.474884
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.511639{col 50}{space 2} .4308267{col 61}{space 1}    1.45{col 70}{space 3}0.147{col 78}{space 4} .8642265{col 91}{space 3} 2.644042
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.146499{col 50}{space 2} .2142211{col 61}{space 1}    0.73{col 70}{space 3}0.464{col 78}{space 4} .7946625{col 91}{space 3} 1.654112
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 2.509639{col 50}{space 2} .6072188{col 61}{space 1}    3.80{col 70}{space 3}0.000{col 78}{space 4} 1.561242{col 91}{space 3} 4.034152
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.649087{col 50}{space 2} .5168764{col 61}{space 1}    1.60{col 70}{space 3}0.111{col 78}{space 4}  .891672{col 91}{space 3} 3.049874
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.619572{col 50}{space 2} .4988888{col 61}{space 1}    1.57{col 70}{space 3}0.118{col 78}{space 4} .8850298{col 91}{space 3} 2.963757
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .6692618{col 50}{space 2} .2259274{col 61}{space 1}   -1.19{col 70}{space 3}0.234{col 78}{space 4}  .345133{col 91}{space 3} 1.297794
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_directionworse i.degree_dummy $controls_demographics $controls_objective if infullmodel_Table2 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}  23{txt},{res}   1299{txt}){col 67}= {res}      4.25
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                   AI_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7356201{col 50}{space 2} .1132835{col 61}{space 1}   -1.99{col 70}{space 3}0.046{col 78}{space 4} .5438144{col 91}{space 3} .9950766
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.643828{col 50}{space 2} .3076717{col 61}{space 1}    2.66{col 70}{space 3}0.008{col 78}{space 4} 1.138657{col 91}{space 3} 2.373122
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.718449{col 50}{space 2} .3686662{col 61}{space 1}    2.52{col 70}{space 3}0.012{col 78}{space 4} 1.128125{col 91}{space 3} 2.617677
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} 1.335884{col 50}{space 2} .1975336{col 61}{space 1}    1.96{col 70}{space 3}0.050{col 78}{space 4} .9995127{col 91}{space 3} 1.785456
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 2.412895{col 50}{space 2} .5516282{col 61}{space 1}    3.85{col 70}{space 3}0.000{col 78}{space 4} 1.540852{col 91}{space 3} 3.778468
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.315665{col 50}{space 2} .2214155{col 61}{space 1}    1.63{col 70}{space 3}0.103{col 78}{space 4} .9457228{col 91}{space 3} 1.830319
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.318259{col 50}{space 2} .4386386{col 61}{space 1}    0.83{col 70}{space 3}0.406{col 78}{space 4}  .686296{col 91}{space 3} 2.532153
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2}  .926384{col 50}{space 2} .2584575{col 61}{space 1}   -0.27{col 70}{space 3}0.784{col 78}{space 4} .5359093{col 91}{space 3} 1.601367
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5966104{col 50}{space 2} .3877683{col 61}{space 1}   -0.79{col 70}{space 3}0.427{col 78}{space 4} .1667029{col 91}{space 3}   2.1352
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.823815{col 50}{space 2} .6373137{col 61}{space 1}    1.72{col 70}{space 3}0.086{col 78}{space 4} .9188914{col 91}{space 3} 3.619906
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 2.220856{col 50}{space 2} .7064391{col 61}{space 1}    2.51{col 70}{space 3}0.012{col 78}{space 4} 1.189901{col 91}{space 3} 4.145053
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.988575{col 50}{space 2} .5775212{col 61}{space 1}    2.37{col 70}{space 3}0.018{col 78}{space 4} 1.124889{col 91}{space 3} 3.515397
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.569265{col 50}{space 2} .4418033{col 61}{space 1}    1.60{col 70}{space 3}0.110{col 78}{space 4} .9033028{col 91}{space 3}  2.72621
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}  .926379{col 50}{space 2} .1941269{col 61}{space 1}   -0.36{col 70}{space 3}0.715{col 78}{space 4} .6141183{col 91}{space 3} 1.397415
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} .8738151{col 50}{space 2} .3061096{col 61}{space 1}   -0.39{col 70}{space 3}0.700{col 78}{space 4} .4394994{col 91}{space 3} 1.737324
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.107186{col 50}{space 2} .4118154{col 61}{space 1}    0.27{col 70}{space 3}0.784{col 78}{space 4} .5337372{col 91}{space 3} 2.296748
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .9733152{col 50}{space 2}   .39149{col 61}{space 1}   -0.07{col 70}{space 3}0.946{col 78}{space 4} .4421467{col 91}{space 3} 2.142597
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .7403207{col 50}{space 2} .1530683{col 61}{space 1}   -1.45{col 70}{space 3}0.146{col 78}{space 4} .4934744{col 91}{space 3} 1.110645
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.080125{col 50}{space 2} .4675268{col 61}{space 1}    0.18{col 70}{space 3}0.859{col 78}{space 4} .4620594{col 91}{space 3} 2.524936
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} .8527179{col 50}{space 2} .3655586{col 61}{space 1}   -0.37{col 70}{space 3}0.710{col 78}{space 4} .3677581{col 91}{space 3} 1.977191
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} 3.897365{col 50}{space 2}  1.55288{col 61}{space 1}    3.41{col 70}{space 3}0.001{col 78}{space 4} 1.783628{col 91}{space 3} 8.516043
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .9586039{col 50}{space 2} .2224665{col 61}{space 1}   -0.18{col 70}{space 3}0.855{col 78}{space 4} .6080186{col 91}{space 3} 1.511338
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.227994{col 50}{space 2} .2313644{col 61}{space 1}    1.09{col 70}{space 3}0.276{col 78}{space 4} .8485466{col 91}{space 3}  1.77712
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .8664103{col 50}{space 2}  .380473{col 61}{space 1}   -0.33{col 70}{space 3}0.744{col 78}{space 4} .3660889{col 91}{space 3} 2.050504
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. 
. ******* ISCO-08 Occupational Group ********
. 
. * Model 1: Worsen 
. svy: logit AI_worsen i.isco08_5group if infullmodel_Table2 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   4{txt},{res}   3959{txt}){col 67}= {res}     11.91
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                         AI_worsen{col 36}{c |} Odds Ratio{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}isco08_5group {c |}
Technician/Associate Professional  {c |}{col 36}{res}{space 2} .9987882{col 48}{space 2}  .112364{col 59}{space 1}   -0.01{col 68}{space 3}0.991{col 76}{space 4} .8010944{col 89}{space 3} 1.245269
{txt}{space 9}Clerical Support Workers  {c |}{col 36}{res}{space 2} 1.370066{col 48}{space 2} .1500356{col 59}{space 1}    2.88{col 68}{space 3}0.004{col 76}{space 4} 1.105346{col 89}{space 3} 1.698185
{txt}{space 8}Service and Sales Workers  {c |}{col 36}{res}{space 2} .5794346{col 48}{space 2} .0804415{col 59}{space 1}   -3.93{col 68}{space 3}0.000{col 76}{space 4} .4413651{col 89}{space 3} .7606955
{txt}{space 13}Manual Working Class  {c |}{col 36}{res}{space 2} .5602667{col 48}{space 2} .0862057{col 59}{space 1}   -3.77{col 68}{space 3}0.000{col 76}{space 4} .4143661{col 89}{space 3} .7575396
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .3398558{col 48}{space 2} .0213553{col 59}{space 1}  -17.18{col 68}{space 3}0.000{col 76}{space 4} .3004636{col 89}{space 3} .3844125
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_worsen i.isco08_5group $controls_demographics if infullmodel_Table2 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      4.27
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.059863{col 50}{space 2} .1222794{col 61}{space 1}    0.50{col 70}{space 3}0.614{col 78}{space 4} .8453065{col 91}{space 3} 1.328878
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.498164{col 50}{space 2} .1787707{col 61}{space 1}    3.39{col 70}{space 3}0.001{col 78}{space 4} 1.185652{col 91}{space 3} 1.893046
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .6336916{col 50}{space 2} .0949955{col 61}{space 1}   -3.04{col 70}{space 3}0.002{col 78}{space 4} .4723206{col 91}{space 3} .8501958
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}  .599223{col 50}{space 2} .1024432{col 61}{space 1}   -3.00{col 70}{space 3}0.003{col 78}{space 4} .4285707{col 91}{space 3} .8378272
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .8927627{col 50}{space 2} .1100534{col 61}{space 1}   -0.92{col 70}{space 3}0.358{col 78}{space 4} .7010902{col 91}{space 3} 1.136837
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .7758053{col 50}{space 2}  .105277{col 61}{space 1}   -1.87{col 70}{space 3}0.061{col 78}{space 4} .5945784{col 91}{space 3}  1.01227
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}  .870817{col 50}{space 2} .0765998{col 61}{space 1}   -1.57{col 70}{space 3}0.116{col 78}{space 4} .7328747{col 91}{space 3} 1.034723
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.282467{col 50}{space 2} .1163864{col 61}{space 1}    2.74{col 70}{space 3}0.006{col 78}{space 4} 1.073431{col 91}{space 3} 1.532209
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.279242{col 50}{space 2}  .134588{col 61}{space 1}    2.34{col 70}{space 3}0.019{col 78}{space 4}  1.04081{col 91}{space 3} 1.572296
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9047129{col 50}{space 2} .0865865{col 61}{space 1}   -1.05{col 70}{space 3}0.295{col 78}{space 4}   .74993{col 91}{space 3} 1.091442
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .8892559{col 50}{space 2} .1604124{col 61}{space 1}   -0.65{col 70}{space 3}0.515{col 78}{space 4} .6243557{col 91}{space 3} 1.266548
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9881668{col 50}{space 2} .1427731{col 61}{space 1}   -0.08{col 70}{space 3}0.934{col 78}{space 4} .7444041{col 91}{space 3} 1.311752
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8492572{col 50}{space 2} .2455706{col 61}{space 1}   -0.57{col 70}{space 3}0.572{col 78}{space 4} .4817605{col 91}{space 3} 1.497088
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.111529{col 50}{space 2} .1852479{col 61}{space 1}    0.63{col 70}{space 3}0.526{col 78}{space 4}  .801707{col 91}{space 3} 1.541082
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .9992331{col 50}{space 2} .1627154{col 61}{space 1}   -0.00{col 70}{space 3}0.996{col 78}{space 4} .7261303{col 91}{space 3} 1.375052
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.011691{col 50}{space 2} .1605068{col 61}{space 1}    0.07{col 70}{space 3}0.942{col 78}{space 4} .7412453{col 91}{space 3}  1.38081
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.081836{col 50}{space 2} .1754481{col 61}{space 1}    0.49{col 70}{space 3}0.628{col 78}{space 4} .7871807{col 91}{space 3} 1.486785
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .3348359{col 50}{space 2} .0651311{col 61}{space 1}   -5.62{col 70}{space 3}0.000{col 78}{space 4} .2286697{col 91}{space 3} .4902927
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_worsen i.isco08_5group $controls_demographics $controls_objective if infullmodel_Table2 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      4.36
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}   .94564{col 50}{space 2} .1172553{col 61}{space 1}   -0.45{col 70}{space 3}0.652{col 78}{space 4} .7415634{col 91}{space 3} 1.205878
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.000113{col 50}{space 2} .1883523{col 61}{space 1}    0.00{col 70}{space 3}1.000{col 78}{space 4} .6913415{col 91}{space 3} 1.446791
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .7392922{col 50}{space 2} .1278909{col 61}{space 1}   -1.75{col 70}{space 3}0.081{col 78}{space 4} .5266486{col 91}{space 3} 1.037794
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .8875048{col 50}{space 2} .2078441{col 61}{space 1}   -0.51{col 70}{space 3}0.610{col 78}{space 4} .5607477{col 91}{space 3} 1.404669
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .9121425{col 50}{space 2}  .111979{col 61}{space 1}   -0.75{col 70}{space 3}0.454{col 78}{space 4} .7170231{col 91}{space 3} 1.160359
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .7963834{col 50}{space 2}  .107791{col 61}{space 1}   -1.68{col 70}{space 3}0.093{col 78}{space 4} .6107681{col 91}{space 3} 1.038408
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .8775447{col 50}{space 2} .0779128{col 61}{space 1}   -1.47{col 70}{space 3}0.141{col 78}{space 4} .7373476{col 91}{space 3} 1.044399
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  1.26827{col 50}{space 2}  .116173{col 61}{space 1}    2.59{col 70}{space 3}0.010{col 78}{space 4} 1.059786{col 91}{space 3} 1.517768
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.270618{col 50}{space 2} .1350353{col 61}{space 1}    2.25{col 70}{space 3}0.024{col 78}{space 4} 1.031634{col 91}{space 3} 1.564964
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9688247{col 50}{space 2} .0945639{col 61}{space 1}   -0.32{col 70}{space 3}0.746{col 78}{space 4} .8000861{col 91}{space 3}  1.17315
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9331544{col 50}{space 2} .1706257{col 61}{space 1}   -0.38{col 70}{space 3}0.705{col 78}{space 4} .6520264{col 91}{space 3} 1.335494
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9994992{col 50}{space 2} .1454519{col 61}{space 1}   -0.00{col 70}{space 3}0.997{col 78}{space 4} .7514043{col 91}{space 3} 1.329509
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8543309{col 50}{space 2} .2446505{col 61}{space 1}   -0.55{col 70}{space 3}0.583{col 78}{space 4} .4873009{col 91}{space 3} 1.497804
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}   1.0992{col 50}{space 2} .1850128{col 61}{space 1}    0.56{col 70}{space 3}0.574{col 78}{space 4} .7902459{col 91}{space 3} 1.528943
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.003071{col 50}{space 2} .1648735{col 61}{space 1}    0.02{col 70}{space 3}0.985{col 78}{space 4}  .726738{col 91}{space 3} 1.384476
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.023778{col 50}{space 2} .1646043{col 61}{space 1}    0.15{col 70}{space 3}0.884{col 78}{space 4} .7469762{col 91}{space 3} 1.403153
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.060449{col 50}{space 2} .1731385{col 61}{space 1}    0.36{col 70}{space 3}0.719{col 78}{space 4} .7699674{col 91}{space 3} 1.460519
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.132371{col 50}{space 2} .1272744{col 61}{space 1}    1.11{col 70}{space 3}0.269{col 78}{space 4} .9084218{col 91}{space 3} 1.411529
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.293345{col 50}{space 2} .3083182{col 61}{space 1}    1.08{col 70}{space 3}0.281{col 78}{space 4} .8104681{col 91}{space 3}  2.06392
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.012834{col 50}{space 2} .2390897{col 61}{space 1}    0.05{col 70}{space 3}0.957{col 78}{space 4} .6375913{col 91}{space 3}  1.60892
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2}  1.47375{col 50}{space 2} .3200393{col 61}{space 1}    1.79{col 70}{space 3}0.074{col 78}{space 4} .9627655{col 91}{space 3} 2.255938
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .8816561{col 50}{space 2}   .12698{col 61}{space 1}   -0.87{col 70}{space 3}0.382{col 78}{space 4} .6647648{col 91}{space 3} 1.169312
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.180469{col 50}{space 2} .1459234{col 61}{space 1}    1.34{col 70}{space 3}0.180{col 78}{space 4} .9264062{col 91}{space 3} 1.504208
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2431594{col 50}{space 2} .0630194{col 61}{space 1}   -5.46{col 70}{space 3}0.000{col 78}{space 4} .1462913{col 91}{space 3} .4041697
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 2: Improve 
. svy: logit AI_improve i.isco08_5group if infullmodel_Table2 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   4{txt},{res}   3959{txt}){col 67}= {res}     14.38
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        AI_improve{col 36}{c |} Odds Ratio{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}isco08_5group {c |}
Technician/Associate Professional  {c |}{col 36}{res}{space 2} .7417614{col 48}{space 2} .1176665{col 59}{space 1}   -1.88{col 68}{space 3}0.060{col 76}{space 4} .5434953{col 89}{space 3} 1.012355
{txt}{space 9}Clerical Support Workers  {c |}{col 36}{res}{space 2} .3316203{col 48}{space 2} .0726532{col 59}{space 1}   -5.04{col 68}{space 3}0.000{col 76}{space 4} .2158233{col 89}{space 3} .5095465
{txt}{space 8}Service and Sales Workers  {c |}{col 36}{res}{space 2}  .281905{col 48}{space 2} .0759406{col 59}{space 1}   -4.70{col 68}{space 3}0.000{col 76}{space 4} .1662391{col 89}{space 3} .4780492
{txt}{space 13}Manual Working Class  {c |}{col 36}{res}{space 2} .3041089{col 48}{space 2} .0783185{col 59}{space 1}   -4.62{col 68}{space 3}0.000{col 76}{space 4} .1835475{col 89}{space 3}   .50386
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2}  .165438{col 48}{space 2} .0127889{col 59}{space 1}  -23.27{col 68}{space 3}0.000{col 76}{space 4} .1421722{col 89}{space 3} .1925112
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_improve i.isco08_5group $controls_demographics if infullmodel_Table2 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      9.00
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                          AI_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .8602597{col 50}{space 2} .1431378{col 61}{space 1}   -0.90{col 70}{space 3}0.366{col 78}{space 4} .6208056{col 91}{space 3} 1.192075
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}  .483637{col 50}{space 2} .1091257{col 61}{space 1}   -3.22{col 70}{space 3}0.001{col 78}{space 4} .3107417{col 91}{space 3} .7527305
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .4243676{col 50}{space 2} .1168096{col 61}{space 1}   -3.11{col 70}{space 3}0.002{col 78}{space 4} .2473845{col 91}{space 3} .7279674
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .3840638{col 50}{space 2} .1048289{col 61}{space 1}   -3.51{col 70}{space 3}0.000{col 78}{space 4} .2249053{col 91}{space 3} .6558537
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .5859237{col 50}{space 2} .0967461{col 61}{space 1}   -3.24{col 70}{space 3}0.001{col 78}{space 4} .4238869{col 91}{space 3} .8099013
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .4313816{col 50}{space 2} .0848871{col 61}{space 1}   -4.27{col 70}{space 3}0.000{col 78}{space 4} .2932994{col 91}{space 3} .6344712
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6274358{col 50}{space 2} .0815649{col 61}{space 1}   -3.59{col 70}{space 3}0.000{col 78}{space 4} .4862747{col 91}{space 3} .8095748
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.753261{col 50}{space 2} .2386836{col 61}{space 1}    4.12{col 70}{space 3}0.000{col 78}{space 4} 1.342552{col 91}{space 3} 2.289613
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .4766213{col 50}{space 2} .0975186{col 61}{space 1}   -3.62{col 70}{space 3}0.000{col 78}{space 4} .3191252{col 91}{space 3} .7118456
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2}  .703992{col 50}{space 2} .1022251{col 61}{space 1}   -2.42{col 70}{space 3}0.016{col 78}{space 4} .5295767{col 91}{space 3} .9358507
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .6980172{col 50}{space 2} .1996649{col 61}{space 1}   -1.26{col 70}{space 3}0.209{col 78}{space 4} .3983906{col 91}{space 3} 1.222991
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.045743{col 50}{space 2} .2381017{col 61}{space 1}    0.20{col 70}{space 3}0.844{col 78}{space 4} .6692037{col 91}{space 3} 1.634148
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.253748{col 50}{space 2} .6701505{col 61}{space 1}    0.42{col 70}{space 3}0.672{col 78}{space 4} .4396317{col 91}{space 3} 3.575457
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5114603{col 50}{space 2} .1552909{col 61}{space 1}   -2.21{col 70}{space 3}0.027{col 78}{space 4} .2820259{col 91}{space 3} .9275449
{txt}{space 34}3  {c |}{col 38}{res}{space 2}  .431203{col 50}{space 2} .1200384{col 61}{space 1}   -3.02{col 70}{space 3}0.003{col 78}{space 4} .2498341{col 91}{space 3} .7442382
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .4891384{col 50}{space 2} .1216417{col 61}{space 1}   -2.88{col 70}{space 3}0.004{col 78}{space 4}   .30039{col 91}{space 3} .7964859
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .6643646{col 50}{space 2} .1598227{col 61}{space 1}   -1.70{col 70}{space 3}0.089{col 78}{space 4} .4145481{col 91}{space 3} 1.064727
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .4719797{col 50}{space 2} .1441441{col 61}{space 1}   -2.46{col 70}{space 3}0.014{col 78}{space 4} .2593487{col 91}{space 3} .8589393
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_improve i.isco08_5group $controls_demographics $controls_objective if infullmodel_Table2 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      7.85
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                          AI_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.010877{col 50}{space 2} .1838329{col 61}{space 1}    0.06{col 70}{space 3}0.953{col 78}{space 4} .7077112{col 91}{space 3} 1.443913
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.090424{col 50}{space 2} .3315547{col 61}{space 1}    0.28{col 70}{space 3}0.776{col 78}{space 4} .6007582{col 91}{space 3} 1.979207
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .7050556{col 50}{space 2} .2294216{col 61}{space 1}   -1.07{col 70}{space 3}0.283{col 78}{space 4} .3725306{col 91}{space 3} 1.334396
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}  .859264{col 50}{space 2} .2912932{col 61}{space 1}   -0.45{col 70}{space 3}0.655{col 78}{space 4} .4420572{col 91}{space 3} 1.670224
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .5967726{col 50}{space 2} .0987241{col 61}{space 1}   -3.12{col 70}{space 3}0.002{col 78}{space 4}  .431471{col 91}{space 3} .8254034
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .4389943{col 50}{space 2} .0868928{col 61}{space 1}   -4.16{col 70}{space 3}0.000{col 78}{space 4} .2977995{col 91}{space 3} .6471334
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6317683{col 50}{space 2} .0833817{col 61}{space 1}   -3.48{col 70}{space 3}0.001{col 78}{space 4} .4877314{col 91}{space 3} .8183422
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.732196{col 50}{space 2} .2368375{col 61}{space 1}    4.02{col 70}{space 3}0.000{col 78}{space 4} 1.324889{col 91}{space 3} 2.264721
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .4842575{col 50}{space 2} .0992227{col 61}{space 1}   -3.54{col 70}{space 3}0.000{col 78}{space 4} .3240521{col 91}{space 3} .7236656
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .6974604{col 50}{space 2} .1028677{col 61}{space 1}   -2.44{col 70}{space 3}0.015{col 78}{space 4} .5223221{col 91}{space 3} .9313239
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .6836831{col 50}{space 2} .1929683{col 61}{space 1}   -1.35{col 70}{space 3}0.178{col 78}{space 4} .3931256{col 91}{space 3}  1.18899
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.129698{col 50}{space 2} .2598135{col 61}{space 1}    0.53{col 70}{space 3}0.596{col 78}{space 4} .7196789{col 91}{space 3} 1.773314
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.339877{col 50}{space 2} .7204718{col 61}{space 1}    0.54{col 70}{space 3}0.586{col 78}{space 4} .4668973{col 91}{space 3} 3.845108
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5083028{col 50}{space 2} .1523285{col 61}{space 1}   -2.26{col 70}{space 3}0.024{col 78}{space 4} .2824594{col 91}{space 3}  .914722
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .4113217{col 50}{space 2} .1135379{col 61}{space 1}   -3.22{col 70}{space 3}0.001{col 78}{space 4} .2394149{col 91}{space 3} .7066626
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .4648291{col 50}{space 2} .1149079{col 61}{space 1}   -3.10{col 70}{space 3}0.002{col 78}{space 4} .2862912{col 91}{space 3} .7547074
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .6062726{col 50}{space 2} .1449004{col 61}{space 1}   -2.09{col 70}{space 3}0.036{col 78}{space 4} .3794607{col 91}{space 3} .9686549
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.600942{col 50}{space 2} .2810704{col 61}{space 1}    2.68{col 70}{space 3}0.007{col 78}{space 4} 1.134718{col 91}{space 3} 2.258723
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.005969{col 50}{space 2} .3909627{col 61}{space 1}    0.02{col 70}{space 3}0.988{col 78}{space 4} .4695373{col 91}{space 3} 2.155257
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2}  1.09556{col 50}{space 2} .4040863{col 61}{space 1}    0.25{col 70}{space 3}0.805{col 78}{space 4} .5315977{col 91}{space 3}  2.25782
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .3170697{col 50}{space 2}  .116649{col 61}{space 1}   -3.12{col 70}{space 3}0.002{col 78}{space 4} .1541361{col 91}{space 3} .6522364
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.003973{col 50}{space 2} .2001391{col 61}{space 1}    0.02{col 70}{space 3}0.984{col 78}{space 4} .6791814{col 91}{space 3} 1.484084
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2}   .88363{col 50}{space 2} .1492986{col 61}{space 1}   -0.73{col 70}{space 3}0.464{col 78}{space 4}  .634464{col 91}{space 3} 1.230648
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .3058212{col 50}{space 2} .1182217{col 61}{space 1}   -3.06{col 70}{space 3}0.002{col 78}{space 4} .1433233{col 91}{space 3} .6525565
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 3: Worsen v Improve
. svy: logit AI_directionworse i.isco08_5group if infullmodel_Table2 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}   4{txt},{res}   1318{txt}){col 67}= {res}      8.39
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                 AI_directionworse{col 36}{c |} Odds Ratio{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}isco08_5group {c |}
Technician/Associate Professional  {c |}{col 36}{res}{space 2} 1.297547{col 48}{space 2} .2309461{col 59}{space 1}    1.46{col 68}{space 3}0.144{col 76}{space 4} .9151279{col 89}{space 3} 1.839775
{txt}{space 9}Clerical Support Workers  {c |}{col 36}{res}{space 2} 3.418554{col 48}{space 2} .7910857{col 59}{space 1}    5.31{col 68}{space 3}0.000{col 76}{space 4} 2.171129{col 89}{space 3} 5.382687
{txt}{space 8}Service and Sales Workers  {c |}{col 36}{res}{space 2} 2.066332{col 48}{space 2} .6044426{col 59}{space 1}    2.48{col 68}{space 3}0.013{col 76}{space 4} 1.164069{col 89}{space 3} 3.667935
{txt}{space 13}Manual Working Class  {c |}{col 36}{res}{space 2}  1.86877{col 48}{space 2} .5363165{col 59}{space 1}    2.18{col 68}{space 3}0.030{col 76}{space 4} 1.064256{col 89}{space 3} 3.281447
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2}  1.78686{col 48}{space 2} .1603329{col 59}{space 1}    6.47{col 68}{space 3}0.000{col 76}{space 4} 1.498453{col 89}{space 3} 2.130776
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_directionworse i.isco08_5group $controls_demographics if infullmodel_Table2 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}  17{txt},{res}   1305{txt}){col 67}= {res}      4.64
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                   AI_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.146499{col 50}{space 2} .2142211{col 61}{space 1}    0.73{col 70}{space 3}0.464{col 78}{space 4} .7946625{col 91}{space 3} 1.654112
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 2.509639{col 50}{space 2} .6072188{col 61}{space 1}    3.80{col 70}{space 3}0.000{col 78}{space 4} 1.561242{col 91}{space 3} 4.034152
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.649087{col 50}{space 2} .5168764{col 61}{space 1}    1.60{col 70}{space 3}0.111{col 78}{space 4}  .891672{col 91}{space 3} 3.049874
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.619572{col 50}{space 2} .4988888{col 61}{space 1}    1.57{col 70}{space 3}0.118{col 78}{space 4} .8850298{col 91}{space 3} 2.963757
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.630793{col 50}{space 2} .3050056{col 61}{space 1}    2.61{col 70}{space 3}0.009{col 78}{space 4} 1.129935{col 91}{space 3} 2.353663
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.731111{col 50}{space 2} .3690646{col 61}{space 1}    2.57{col 70}{space 3}0.010{col 78}{space 4} 1.139427{col 91}{space 3} 2.630047
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} 1.343212{col 50}{space 2} .1961699{col 61}{space 1}    2.02{col 70}{space 3}0.044{col 78}{space 4} 1.008595{col 91}{space 3} 1.788845
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7325523{col 50}{space 2} .1120903{col 61}{space 1}   -2.03{col 70}{space 3}0.042{col 78}{space 4} .5425927{col 91}{space 3}  .989016
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 2.387622{col 50}{space 2} .5475305{col 61}{space 1}    3.80{col 70}{space 3}0.000{col 78}{space 4}  1.52261{col 91}{space 3} 3.744057
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.240947{col 50}{space 2} .2031517{col 61}{space 1}    1.32{col 70}{space 3}0.188{col 78}{space 4} .9000731{col 91}{space 3} 1.710916
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}  1.22502{col 50}{space 2} .4068413{col 61}{space 1}    0.61{col 70}{space 3}0.541{col 78}{space 4} .6385446{col 91}{space 3} 2.350146
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.053222{col 50}{space 2} .2908356{col 61}{space 1}    0.19{col 70}{space 3}0.851{col 78}{space 4} .6127091{col 91}{space 3} 1.810445
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .6364657{col 50}{space 2}  .396653{col 61}{space 1}   -0.72{col 70}{space 3}0.469{col 78}{space 4} .1874172{col 91}{space 3} 2.161426
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.921637{col 50}{space 2} .6822025{col 61}{space 1}    1.84{col 70}{space 3}0.066{col 78}{space 4} .9576527{col 91}{space 3} 3.855977
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 2.194025{col 50}{space 2} .7060782{col 61}{space 1}    2.44{col 70}{space 3}0.015{col 78}{space 4} 1.166965{col 91}{space 3} 4.125013
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.954344{col 50}{space 2} .5733307{col 61}{space 1}    2.28{col 70}{space 3}0.023{col 78}{space 4} 1.099161{col 91}{space 3} 3.474884
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.511639{col 50}{space 2} .4308267{col 61}{space 1}    1.45{col 70}{space 3}0.147{col 78}{space 4} .8642265{col 91}{space 3} 2.644042
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .6692618{col 50}{space 2} .2259274{col 61}{space 1}   -1.19{col 70}{space 3}0.234{col 78}{space 4}  .345133{col 91}{space 3} 1.297794
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_directionworse i.isco08_5group $controls_demographics $controls_objective if infullmodel_Table2 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}  23{txt},{res}   1299{txt}){col 67}= {res}      4.25
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                   AI_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}  .926379{col 50}{space 2} .1941269{col 61}{space 1}   -0.36{col 70}{space 3}0.715{col 78}{space 4} .6141183{col 91}{space 3} 1.397415
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} .8738151{col 50}{space 2} .3061096{col 61}{space 1}   -0.39{col 70}{space 3}0.700{col 78}{space 4} .4394994{col 91}{space 3} 1.737324
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.107186{col 50}{space 2} .4118154{col 61}{space 1}    0.27{col 70}{space 3}0.784{col 78}{space 4} .5337372{col 91}{space 3} 2.296748
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .9733152{col 50}{space 2}   .39149{col 61}{space 1}   -0.07{col 70}{space 3}0.946{col 78}{space 4} .4421467{col 91}{space 3} 2.142597
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.643828{col 50}{space 2} .3076717{col 61}{space 1}    2.66{col 70}{space 3}0.008{col 78}{space 4} 1.138657{col 91}{space 3} 2.373122
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.718449{col 50}{space 2} .3686662{col 61}{space 1}    2.52{col 70}{space 3}0.012{col 78}{space 4} 1.128125{col 91}{space 3} 2.617677
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} 1.335884{col 50}{space 2} .1975336{col 61}{space 1}    1.96{col 70}{space 3}0.050{col 78}{space 4} .9995127{col 91}{space 3} 1.785456
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7356201{col 50}{space 2} .1132835{col 61}{space 1}   -1.99{col 70}{space 3}0.046{col 78}{space 4} .5438144{col 91}{space 3} .9950766
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 2.412895{col 50}{space 2} .5516282{col 61}{space 1}    3.85{col 70}{space 3}0.000{col 78}{space 4} 1.540852{col 91}{space 3} 3.778468
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.315665{col 50}{space 2} .2214155{col 61}{space 1}    1.63{col 70}{space 3}0.103{col 78}{space 4} .9457228{col 91}{space 3} 1.830319
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.318259{col 50}{space 2} .4386386{col 61}{space 1}    0.83{col 70}{space 3}0.406{col 78}{space 4}  .686296{col 91}{space 3} 2.532153
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2}  .926384{col 50}{space 2} .2584575{col 61}{space 1}   -0.27{col 70}{space 3}0.784{col 78}{space 4} .5359093{col 91}{space 3} 1.601367
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5966104{col 50}{space 2} .3877683{col 61}{space 1}   -0.79{col 70}{space 3}0.427{col 78}{space 4} .1667029{col 91}{space 3}   2.1352
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.823815{col 50}{space 2} .6373137{col 61}{space 1}    1.72{col 70}{space 3}0.086{col 78}{space 4} .9188914{col 91}{space 3} 3.619906
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 2.220856{col 50}{space 2} .7064391{col 61}{space 1}    2.51{col 70}{space 3}0.012{col 78}{space 4} 1.189901{col 91}{space 3} 4.145053
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.988575{col 50}{space 2} .5775212{col 61}{space 1}    2.37{col 70}{space 3}0.018{col 78}{space 4} 1.124889{col 91}{space 3} 3.515397
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.569265{col 50}{space 2} .4418033{col 61}{space 1}    1.60{col 70}{space 3}0.110{col 78}{space 4} .9033028{col 91}{space 3}  2.72621
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .7403207{col 50}{space 2} .1530683{col 61}{space 1}   -1.45{col 70}{space 3}0.146{col 78}{space 4} .4934744{col 91}{space 3} 1.110645
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.080125{col 50}{space 2} .4675268{col 61}{space 1}    0.18{col 70}{space 3}0.859{col 78}{space 4} .4620594{col 91}{space 3} 2.524936
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} .8527179{col 50}{space 2} .3655586{col 61}{space 1}   -0.37{col 70}{space 3}0.710{col 78}{space 4} .3677581{col 91}{space 3} 1.977191
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} 3.897365{col 50}{space 2}  1.55288{col 61}{space 1}    3.41{col 70}{space 3}0.001{col 78}{space 4} 1.783628{col 91}{space 3} 8.516043
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .9586039{col 50}{space 2} .2224665{col 61}{space 1}   -0.18{col 70}{space 3}0.855{col 78}{space 4} .6080186{col 91}{space 3} 1.511338
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.227994{col 50}{space 2} .2313644{col 61}{space 1}    1.09{col 70}{space 3}0.276{col 78}{space 4} .8485466{col 91}{space 3}  1.77712
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .8664103{col 50}{space 2}  .380473{col 61}{space 1}   -0.33{col 70}{space 3}0.744{col 78}{space 4} .3660889{col 91}{space 3} 2.050504
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. 
. ******* Employment Sector Group ********
. 
. * Model 1: Worsen 
. svy: logit AI_worsen i.employsector5 if infullmodel_Table2 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   4{txt},{res}   3959{txt}){col 67}= {res}      0.24
{txt}{col 49}Prob > F{col 67}= {res}    0.9163

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9625978{col 50}{space 2} .0874099{col 61}{space 1}   -0.42{col 70}{space 3}0.675{col 78}{space 4} .8056136{col 91}{space 3} 1.150172
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9845752{col 50}{space 2} .1736077{col 61}{space 1}   -0.09{col 70}{space 3}0.930{col 78}{space 4} .6968075{col 91}{space 3} 1.391185
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9369189{col 50}{space 2} .1289741{col 61}{space 1}   -0.47{col 70}{space 3}0.636{col 78}{space 4} .7153059{col 91}{space 3} 1.227191
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2}   .78218{col 50}{space 2} .2220287{col 61}{space 1}   -0.87{col 70}{space 3}0.387{col 78}{space 4} .4483445{col 91}{space 3} 1.364588
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .3176495{col 50}{space 2} .0184933{col 61}{space 1}  -19.70{col 70}{space 3}0.000{col 78}{space 4} .2833849{col 91}{space 3} .3560571
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_worsen i.employsector5 $controls_demographics if infullmodel_Table2 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      4.27
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9047129{col 50}{space 2} .0865865{col 61}{space 1}   -1.05{col 70}{space 3}0.295{col 78}{space 4}   .74993{col 91}{space 3} 1.091442
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .8892559{col 50}{space 2} .1604124{col 61}{space 1}   -0.65{col 70}{space 3}0.515{col 78}{space 4} .6243557{col 91}{space 3} 1.266548
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9881668{col 50}{space 2} .1427731{col 61}{space 1}   -0.08{col 70}{space 3}0.934{col 78}{space 4} .7444041{col 91}{space 3} 1.311752
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8492572{col 50}{space 2} .2455706{col 61}{space 1}   -0.57{col 70}{space 3}0.572{col 78}{space 4} .4817605{col 91}{space 3} 1.497088
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .8927627{col 50}{space 2} .1100534{col 61}{space 1}   -0.92{col 70}{space 3}0.358{col 78}{space 4} .7010902{col 91}{space 3} 1.136837
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .7758053{col 50}{space 2}  .105277{col 61}{space 1}   -1.87{col 70}{space 3}0.061{col 78}{space 4} .5945784{col 91}{space 3}  1.01227
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}  .870817{col 50}{space 2} .0765998{col 61}{space 1}   -1.57{col 70}{space 3}0.116{col 78}{space 4} .7328747{col 91}{space 3} 1.034723
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.282467{col 50}{space 2} .1163864{col 61}{space 1}    2.74{col 70}{space 3}0.006{col 78}{space 4} 1.073431{col 91}{space 3} 1.532209
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.279242{col 50}{space 2}  .134588{col 61}{space 1}    2.34{col 70}{space 3}0.019{col 78}{space 4}  1.04081{col 91}{space 3} 1.572296
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.111529{col 50}{space 2} .1852479{col 61}{space 1}    0.63{col 70}{space 3}0.526{col 78}{space 4}  .801707{col 91}{space 3} 1.541082
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .9992331{col 50}{space 2} .1627154{col 61}{space 1}   -0.00{col 70}{space 3}0.996{col 78}{space 4} .7261303{col 91}{space 3} 1.375052
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.011691{col 50}{space 2} .1605068{col 61}{space 1}    0.07{col 70}{space 3}0.942{col 78}{space 4} .7412453{col 91}{space 3}  1.38081
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.081836{col 50}{space 2} .1754481{col 61}{space 1}    0.49{col 70}{space 3}0.628{col 78}{space 4} .7871807{col 91}{space 3} 1.486785
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.059863{col 50}{space 2} .1222794{col 61}{space 1}    0.50{col 70}{space 3}0.614{col 78}{space 4} .8453065{col 91}{space 3} 1.328878
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.498164{col 50}{space 2} .1787707{col 61}{space 1}    3.39{col 70}{space 3}0.001{col 78}{space 4} 1.185652{col 91}{space 3} 1.893046
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .6336916{col 50}{space 2} .0949955{col 61}{space 1}   -3.04{col 70}{space 3}0.002{col 78}{space 4} .4723206{col 91}{space 3} .8501958
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}  .599223{col 50}{space 2} .1024432{col 61}{space 1}   -3.00{col 70}{space 3}0.003{col 78}{space 4} .4285707{col 91}{space 3} .8378272
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .3348359{col 50}{space 2} .0651311{col 61}{space 1}   -5.62{col 70}{space 3}0.000{col 78}{space 4} .2286697{col 91}{space 3} .4902927
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_worsen i.employsector5 $controls_demographics $controls_objective if infullmodel_Table2 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      4.36
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9688247{col 50}{space 2} .0945639{col 61}{space 1}   -0.32{col 70}{space 3}0.746{col 78}{space 4} .8000861{col 91}{space 3}  1.17315
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9331544{col 50}{space 2} .1706257{col 61}{space 1}   -0.38{col 70}{space 3}0.705{col 78}{space 4} .6520264{col 91}{space 3} 1.335494
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9994992{col 50}{space 2} .1454519{col 61}{space 1}   -0.00{col 70}{space 3}0.997{col 78}{space 4} .7514043{col 91}{space 3} 1.329509
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8543309{col 50}{space 2} .2446505{col 61}{space 1}   -0.55{col 70}{space 3}0.583{col 78}{space 4} .4873009{col 91}{space 3} 1.497804
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .9121425{col 50}{space 2}  .111979{col 61}{space 1}   -0.75{col 70}{space 3}0.454{col 78}{space 4} .7170231{col 91}{space 3} 1.160359
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .7963834{col 50}{space 2}  .107791{col 61}{space 1}   -1.68{col 70}{space 3}0.093{col 78}{space 4} .6107681{col 91}{space 3} 1.038408
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .8775447{col 50}{space 2} .0779128{col 61}{space 1}   -1.47{col 70}{space 3}0.141{col 78}{space 4} .7373476{col 91}{space 3} 1.044399
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  1.26827{col 50}{space 2}  .116173{col 61}{space 1}    2.59{col 70}{space 3}0.010{col 78}{space 4} 1.059786{col 91}{space 3} 1.517768
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.270618{col 50}{space 2} .1350353{col 61}{space 1}    2.25{col 70}{space 3}0.024{col 78}{space 4} 1.031634{col 91}{space 3} 1.564964
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}   1.0992{col 50}{space 2} .1850128{col 61}{space 1}    0.56{col 70}{space 3}0.574{col 78}{space 4} .7902459{col 91}{space 3} 1.528943
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.003071{col 50}{space 2} .1648735{col 61}{space 1}    0.02{col 70}{space 3}0.985{col 78}{space 4}  .726738{col 91}{space 3} 1.384476
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.023778{col 50}{space 2} .1646043{col 61}{space 1}    0.15{col 70}{space 3}0.884{col 78}{space 4} .7469762{col 91}{space 3} 1.403153
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.060449{col 50}{space 2} .1731385{col 61}{space 1}    0.36{col 70}{space 3}0.719{col 78}{space 4} .7699674{col 91}{space 3} 1.460519
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}   .94564{col 50}{space 2} .1172553{col 61}{space 1}   -0.45{col 70}{space 3}0.652{col 78}{space 4} .7415634{col 91}{space 3} 1.205878
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.000113{col 50}{space 2} .1883523{col 61}{space 1}    0.00{col 70}{space 3}1.000{col 78}{space 4} .6913415{col 91}{space 3} 1.446791
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .7392922{col 50}{space 2} .1278909{col 61}{space 1}   -1.75{col 70}{space 3}0.081{col 78}{space 4} .5266486{col 91}{space 3} 1.037794
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .8875048{col 50}{space 2} .2078441{col 61}{space 1}   -0.51{col 70}{space 3}0.610{col 78}{space 4} .5607477{col 91}{space 3} 1.404669
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.132371{col 50}{space 2} .1272744{col 61}{space 1}    1.11{col 70}{space 3}0.269{col 78}{space 4} .9084218{col 91}{space 3} 1.411529
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.293345{col 50}{space 2} .3083182{col 61}{space 1}    1.08{col 70}{space 3}0.281{col 78}{space 4} .8104681{col 91}{space 3}  2.06392
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.012834{col 50}{space 2} .2390897{col 61}{space 1}    0.05{col 70}{space 3}0.957{col 78}{space 4} .6375913{col 91}{space 3}  1.60892
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2}  1.47375{col 50}{space 2} .3200393{col 61}{space 1}    1.79{col 70}{space 3}0.074{col 78}{space 4} .9627655{col 91}{space 3} 2.255938
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .8816561{col 50}{space 2}   .12698{col 61}{space 1}   -0.87{col 70}{space 3}0.382{col 78}{space 4} .6647648{col 91}{space 3} 1.169312
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.180469{col 50}{space 2} .1459234{col 61}{space 1}    1.34{col 70}{space 3}0.180{col 78}{space 4} .9264062{col 91}{space 3} 1.504208
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2431594{col 50}{space 2} .0630194{col 61}{space 1}   -5.46{col 70}{space 3}0.000{col 78}{space 4} .1462913{col 91}{space 3} .4041697
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 2: Improve 
. svy: logit AI_improve i.employsector5 if infullmodel_Table2 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   4{txt},{res}   3959{txt}){col 67}= {res}      2.07
{txt}{col 49}Prob > F{col 67}= {res}    0.0824

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                          AI_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .7122379{col 50}{space 2} .0964566{col 61}{space 1}   -2.51{col 70}{space 3}0.012{col 78}{space 4} .5461523{col 91}{space 3} .9288304
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .6328008{col 50}{space 2}  .169174{col 61}{space 1}   -1.71{col 70}{space 3}0.087{col 78}{space 4} .3746588{col 91}{space 3} 1.068804
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .7599879{col 50}{space 2}  .164147{col 61}{space 1}   -1.27{col 70}{space 3}0.204{col 78}{space 4} .4976256{col 91}{space 3} 1.160675
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2}  .783538{col 50}{space 2} .3782862{col 61}{space 1}   -0.51{col 70}{space 3}0.613{col 78}{space 4}  .304075{col 91}{space 3} 2.019014
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .1215165{col 50}{space 2}  .010034{col 61}{space 1}  -25.53{col 70}{space 3}0.000{col 78}{space 4}  .103354{col 91}{space 3} .1428707
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_improve i.employsector5 $controls_demographics if infullmodel_Table2 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      9.00
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                          AI_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2}  .703992{col 50}{space 2} .1022251{col 61}{space 1}   -2.42{col 70}{space 3}0.016{col 78}{space 4} .5295767{col 91}{space 3} .9358507
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .6980172{col 50}{space 2} .1996649{col 61}{space 1}   -1.26{col 70}{space 3}0.209{col 78}{space 4} .3983906{col 91}{space 3} 1.222991
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.045743{col 50}{space 2} .2381017{col 61}{space 1}    0.20{col 70}{space 3}0.844{col 78}{space 4} .6692037{col 91}{space 3} 1.634148
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.253748{col 50}{space 2} .6701505{col 61}{space 1}    0.42{col 70}{space 3}0.672{col 78}{space 4} .4396317{col 91}{space 3} 3.575457
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .5859237{col 50}{space 2} .0967461{col 61}{space 1}   -3.24{col 70}{space 3}0.001{col 78}{space 4} .4238869{col 91}{space 3} .8099013
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .4313816{col 50}{space 2} .0848871{col 61}{space 1}   -4.27{col 70}{space 3}0.000{col 78}{space 4} .2932994{col 91}{space 3} .6344712
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6274358{col 50}{space 2} .0815649{col 61}{space 1}   -3.59{col 70}{space 3}0.000{col 78}{space 4} .4862747{col 91}{space 3} .8095748
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.753261{col 50}{space 2} .2386836{col 61}{space 1}    4.12{col 70}{space 3}0.000{col 78}{space 4} 1.342552{col 91}{space 3} 2.289613
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .4766213{col 50}{space 2} .0975186{col 61}{space 1}   -3.62{col 70}{space 3}0.000{col 78}{space 4} .3191252{col 91}{space 3} .7118456
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5114603{col 50}{space 2} .1552909{col 61}{space 1}   -2.21{col 70}{space 3}0.027{col 78}{space 4} .2820259{col 91}{space 3} .9275449
{txt}{space 34}3  {c |}{col 38}{res}{space 2}  .431203{col 50}{space 2} .1200384{col 61}{space 1}   -3.02{col 70}{space 3}0.003{col 78}{space 4} .2498341{col 91}{space 3} .7442382
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .4891384{col 50}{space 2} .1216417{col 61}{space 1}   -2.88{col 70}{space 3}0.004{col 78}{space 4}   .30039{col 91}{space 3} .7964859
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .6643646{col 50}{space 2} .1598227{col 61}{space 1}   -1.70{col 70}{space 3}0.089{col 78}{space 4} .4145481{col 91}{space 3} 1.064727
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .8602597{col 50}{space 2} .1431378{col 61}{space 1}   -0.90{col 70}{space 3}0.366{col 78}{space 4} .6208056{col 91}{space 3} 1.192075
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}  .483637{col 50}{space 2} .1091257{col 61}{space 1}   -3.22{col 70}{space 3}0.001{col 78}{space 4} .3107417{col 91}{space 3} .7527305
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .4243676{col 50}{space 2} .1168096{col 61}{space 1}   -3.11{col 70}{space 3}0.002{col 78}{space 4} .2473845{col 91}{space 3} .7279674
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .3840638{col 50}{space 2} .1048289{col 61}{space 1}   -3.51{col 70}{space 3}0.000{col 78}{space 4} .2249053{col 91}{space 3} .6558537
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .4719797{col 50}{space 2} .1441441{col 61}{space 1}   -2.46{col 70}{space 3}0.014{col 78}{space 4} .2593487{col 91}{space 3} .8589393
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_improve i.employsector5 $controls_demographics $controls_objective if infullmodel_Table2 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      7.85
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                          AI_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .6974604{col 50}{space 2} .1028677{col 61}{space 1}   -2.44{col 70}{space 3}0.015{col 78}{space 4} .5223221{col 91}{space 3} .9313239
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .6836831{col 50}{space 2} .1929683{col 61}{space 1}   -1.35{col 70}{space 3}0.178{col 78}{space 4} .3931256{col 91}{space 3}  1.18899
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.129698{col 50}{space 2} .2598135{col 61}{space 1}    0.53{col 70}{space 3}0.596{col 78}{space 4} .7196789{col 91}{space 3} 1.773314
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.339877{col 50}{space 2} .7204718{col 61}{space 1}    0.54{col 70}{space 3}0.586{col 78}{space 4} .4668973{col 91}{space 3} 3.845108
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .5967726{col 50}{space 2} .0987241{col 61}{space 1}   -3.12{col 70}{space 3}0.002{col 78}{space 4}  .431471{col 91}{space 3} .8254034
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .4389943{col 50}{space 2} .0868928{col 61}{space 1}   -4.16{col 70}{space 3}0.000{col 78}{space 4} .2977995{col 91}{space 3} .6471334
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6317683{col 50}{space 2} .0833817{col 61}{space 1}   -3.48{col 70}{space 3}0.001{col 78}{space 4} .4877314{col 91}{space 3} .8183422
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.732196{col 50}{space 2} .2368375{col 61}{space 1}    4.02{col 70}{space 3}0.000{col 78}{space 4} 1.324889{col 91}{space 3} 2.264721
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .4842575{col 50}{space 2} .0992227{col 61}{space 1}   -3.54{col 70}{space 3}0.000{col 78}{space 4} .3240521{col 91}{space 3} .7236656
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5083028{col 50}{space 2} .1523285{col 61}{space 1}   -2.26{col 70}{space 3}0.024{col 78}{space 4} .2824594{col 91}{space 3}  .914722
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .4113217{col 50}{space 2} .1135379{col 61}{space 1}   -3.22{col 70}{space 3}0.001{col 78}{space 4} .2394149{col 91}{space 3} .7066626
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .4648291{col 50}{space 2} .1149079{col 61}{space 1}   -3.10{col 70}{space 3}0.002{col 78}{space 4} .2862912{col 91}{space 3} .7547074
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .6062726{col 50}{space 2} .1449004{col 61}{space 1}   -2.09{col 70}{space 3}0.036{col 78}{space 4} .3794607{col 91}{space 3} .9686549
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.010877{col 50}{space 2} .1838329{col 61}{space 1}    0.06{col 70}{space 3}0.953{col 78}{space 4} .7077112{col 91}{space 3} 1.443913
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.090424{col 50}{space 2} .3315547{col 61}{space 1}    0.28{col 70}{space 3}0.776{col 78}{space 4} .6007582{col 91}{space 3} 1.979207
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .7050556{col 50}{space 2} .2294216{col 61}{space 1}   -1.07{col 70}{space 3}0.283{col 78}{space 4} .3725306{col 91}{space 3} 1.334396
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}  .859264{col 50}{space 2} .2912932{col 61}{space 1}   -0.45{col 70}{space 3}0.655{col 78}{space 4} .4420572{col 91}{space 3} 1.670224
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.600942{col 50}{space 2} .2810704{col 61}{space 1}    2.68{col 70}{space 3}0.007{col 78}{space 4} 1.134718{col 91}{space 3} 2.258723
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.005969{col 50}{space 2} .3909627{col 61}{space 1}    0.02{col 70}{space 3}0.988{col 78}{space 4} .4695373{col 91}{space 3} 2.155257
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2}  1.09556{col 50}{space 2} .4040863{col 61}{space 1}    0.25{col 70}{space 3}0.805{col 78}{space 4} .5315977{col 91}{space 3}  2.25782
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .3170697{col 50}{space 2}  .116649{col 61}{space 1}   -3.12{col 70}{space 3}0.002{col 78}{space 4} .1541361{col 91}{space 3} .6522364
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.003973{col 50}{space 2} .2001391{col 61}{space 1}    0.02{col 70}{space 3}0.984{col 78}{space 4} .6791814{col 91}{space 3} 1.484084
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2}   .88363{col 50}{space 2} .1492986{col 61}{space 1}   -0.73{col 70}{space 3}0.464{col 78}{space 4}  .634464{col 91}{space 3} 1.230648
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .3058212{col 50}{space 2} .1182217{col 61}{space 1}   -3.06{col 70}{space 3}0.002{col 78}{space 4} .1433233{col 91}{space 3} .6525565
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 3: Worsen v Improve
. svy: logit AI_directionworse i.employsector5 if infullmodel_Table2 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}   4{txt},{res}   1318{txt}){col 67}= {res}      1.14
{txt}{col 49}Prob > F{col 67}= {res}    0.3355

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                   AI_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.321286{col 50}{space 2} .2002341{col 61}{space 1}    1.84{col 70}{space 3}0.066{col 78}{space 4} .9814845{col 91}{space 3} 1.778732
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.499574{col 50}{space 2} .4472377{col 61}{space 1}    1.36{col 70}{space 3}0.175{col 78}{space 4} .8353528{col 91}{space 3} 2.691942
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2}  1.21929{col 50}{space 2} .2905153{col 61}{space 1}    0.83{col 70}{space 3}0.405{col 78}{space 4} .7640269{col 91}{space 3} 1.945832
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.028881{col 50}{space 2} .5462679{col 61}{space 1}    0.05{col 70}{space 3}0.957{col 78}{space 4} .3630937{col 91}{space 3} 2.915489
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 2.224942{col 50}{space 2} .2068271{col 61}{space 1}    8.60{col 70}{space 3}0.000{col 78}{space 4} 1.854043{col 91}{space 3} 2.670039
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_directionworse i.employsector5 $controls_demographics if infullmodel_Table2 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}  17{txt},{res}   1305{txt}){col 67}= {res}      4.64
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                   AI_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.240947{col 50}{space 2} .2031517{col 61}{space 1}    1.32{col 70}{space 3}0.188{col 78}{space 4} .9000731{col 91}{space 3} 1.710916
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}  1.22502{col 50}{space 2} .4068413{col 61}{space 1}    0.61{col 70}{space 3}0.541{col 78}{space 4} .6385446{col 91}{space 3} 2.350146
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.053222{col 50}{space 2} .2908356{col 61}{space 1}    0.19{col 70}{space 3}0.851{col 78}{space 4} .6127091{col 91}{space 3} 1.810445
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .6364657{col 50}{space 2}  .396653{col 61}{space 1}   -0.72{col 70}{space 3}0.469{col 78}{space 4} .1874172{col 91}{space 3} 2.161426
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.630793{col 50}{space 2} .3050056{col 61}{space 1}    2.61{col 70}{space 3}0.009{col 78}{space 4} 1.129935{col 91}{space 3} 2.353663
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.731111{col 50}{space 2} .3690646{col 61}{space 1}    2.57{col 70}{space 3}0.010{col 78}{space 4} 1.139427{col 91}{space 3} 2.630047
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} 1.343212{col 50}{space 2} .1961699{col 61}{space 1}    2.02{col 70}{space 3}0.044{col 78}{space 4} 1.008595{col 91}{space 3} 1.788845
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7325523{col 50}{space 2} .1120903{col 61}{space 1}   -2.03{col 70}{space 3}0.042{col 78}{space 4} .5425927{col 91}{space 3}  .989016
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 2.387622{col 50}{space 2} .5475305{col 61}{space 1}    3.80{col 70}{space 3}0.000{col 78}{space 4}  1.52261{col 91}{space 3} 3.744057
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.921637{col 50}{space 2} .6822025{col 61}{space 1}    1.84{col 70}{space 3}0.066{col 78}{space 4} .9576527{col 91}{space 3} 3.855977
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 2.194025{col 50}{space 2} .7060782{col 61}{space 1}    2.44{col 70}{space 3}0.015{col 78}{space 4} 1.166965{col 91}{space 3} 4.125013
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.954344{col 50}{space 2} .5733307{col 61}{space 1}    2.28{col 70}{space 3}0.023{col 78}{space 4} 1.099161{col 91}{space 3} 3.474884
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.511639{col 50}{space 2} .4308267{col 61}{space 1}    1.45{col 70}{space 3}0.147{col 78}{space 4} .8642265{col 91}{space 3} 2.644042
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.146499{col 50}{space 2} .2142211{col 61}{space 1}    0.73{col 70}{space 3}0.464{col 78}{space 4} .7946625{col 91}{space 3} 1.654112
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 2.509639{col 50}{space 2} .6072188{col 61}{space 1}    3.80{col 70}{space 3}0.000{col 78}{space 4} 1.561242{col 91}{space 3} 4.034152
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.649087{col 50}{space 2} .5168764{col 61}{space 1}    1.60{col 70}{space 3}0.111{col 78}{space 4}  .891672{col 91}{space 3} 3.049874
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.619572{col 50}{space 2} .4988888{col 61}{space 1}    1.57{col 70}{space 3}0.118{col 78}{space 4} .8850298{col 91}{space 3} 2.963757
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .6692618{col 50}{space 2} .2259274{col 61}{space 1}   -1.19{col 70}{space 3}0.234{col 78}{space 4}  .345133{col 91}{space 3} 1.297794
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_directionworse i.employsector5 $controls_demographics $controls_objective if infullmodel_Table2 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}  23{txt},{res}   1299{txt}){col 67}= {res}      4.25
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                   AI_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.315665{col 50}{space 2} .2214155{col 61}{space 1}    1.63{col 70}{space 3}0.103{col 78}{space 4} .9457228{col 91}{space 3} 1.830319
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.318259{col 50}{space 2} .4386386{col 61}{space 1}    0.83{col 70}{space 3}0.406{col 78}{space 4}  .686296{col 91}{space 3} 2.532153
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2}  .926384{col 50}{space 2} .2584575{col 61}{space 1}   -0.27{col 70}{space 3}0.784{col 78}{space 4} .5359093{col 91}{space 3} 1.601367
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5966104{col 50}{space 2} .3877683{col 61}{space 1}   -0.79{col 70}{space 3}0.427{col 78}{space 4} .1667029{col 91}{space 3}   2.1352
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.643828{col 50}{space 2} .3076717{col 61}{space 1}    2.66{col 70}{space 3}0.008{col 78}{space 4} 1.138657{col 91}{space 3} 2.373122
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.718449{col 50}{space 2} .3686662{col 61}{space 1}    2.52{col 70}{space 3}0.012{col 78}{space 4} 1.128125{col 91}{space 3} 2.617677
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} 1.335884{col 50}{space 2} .1975336{col 61}{space 1}    1.96{col 70}{space 3}0.050{col 78}{space 4} .9995127{col 91}{space 3} 1.785456
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7356201{col 50}{space 2} .1132835{col 61}{space 1}   -1.99{col 70}{space 3}0.046{col 78}{space 4} .5438144{col 91}{space 3} .9950766
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 2.412895{col 50}{space 2} .5516282{col 61}{space 1}    3.85{col 70}{space 3}0.000{col 78}{space 4} 1.540852{col 91}{space 3} 3.778468
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.823815{col 50}{space 2} .6373137{col 61}{space 1}    1.72{col 70}{space 3}0.086{col 78}{space 4} .9188914{col 91}{space 3} 3.619906
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 2.220856{col 50}{space 2} .7064391{col 61}{space 1}    2.51{col 70}{space 3}0.012{col 78}{space 4} 1.189901{col 91}{space 3} 4.145053
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.988575{col 50}{space 2} .5775212{col 61}{space 1}    2.37{col 70}{space 3}0.018{col 78}{space 4} 1.124889{col 91}{space 3} 3.515397
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.569265{col 50}{space 2} .4418033{col 61}{space 1}    1.60{col 70}{space 3}0.110{col 78}{space 4} .9033028{col 91}{space 3}  2.72621
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}  .926379{col 50}{space 2} .1941269{col 61}{space 1}   -0.36{col 70}{space 3}0.715{col 78}{space 4} .6141183{col 91}{space 3} 1.397415
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} .8738151{col 50}{space 2} .3061096{col 61}{space 1}   -0.39{col 70}{space 3}0.700{col 78}{space 4} .4394994{col 91}{space 3} 1.737324
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.107186{col 50}{space 2} .4118154{col 61}{space 1}    0.27{col 70}{space 3}0.784{col 78}{space 4} .5337372{col 91}{space 3} 2.296748
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .9733152{col 50}{space 2}   .39149{col 61}{space 1}   -0.07{col 70}{space 3}0.946{col 78}{space 4} .4421467{col 91}{space 3} 2.142597
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .7403207{col 50}{space 2} .1530683{col 61}{space 1}   -1.45{col 70}{space 3}0.146{col 78}{space 4} .4934744{col 91}{space 3} 1.110645
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.080125{col 50}{space 2} .4675268{col 61}{space 1}    0.18{col 70}{space 3}0.859{col 78}{space 4} .4620594{col 91}{space 3} 2.524936
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} .8527179{col 50}{space 2} .3655586{col 61}{space 1}   -0.37{col 70}{space 3}0.710{col 78}{space 4} .3677581{col 91}{space 3} 1.977191
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} 3.897365{col 50}{space 2}  1.55288{col 61}{space 1}    3.41{col 70}{space 3}0.001{col 78}{space 4} 1.783628{col 91}{space 3} 8.516043
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .9586039{col 50}{space 2} .2224665{col 61}{space 1}   -0.18{col 70}{space 3}0.855{col 78}{space 4} .6080186{col 91}{space 3} 1.511338
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.227994{col 50}{space 2} .2313644{col 61}{space 1}    1.09{col 70}{space 3}0.276{col 78}{space 4} .8485466{col 91}{space 3}  1.77712
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .8664103{col 50}{space 2}  .380473{col 61}{space 1}   -0.33{col 70}{space 3}0.744{col 78}{space 4} .3660889{col 91}{space 3} 2.050504
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. 
. ******* Equivalised HH Income ********
. 
. 
. * Model 1: Worsen 
. svy: logit AI_worsen i.equivincomeHHquintile_HBAI_imp if infullmodel_Table2 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   4{txt},{res}   3959{txt}){col 67}= {res}      1.08
{txt}{col 49}Prob > F{col 67}= {res}    0.3663

{txt}{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 32}{c |}{col 44}  Linearized
{col 1}                     AI_worsen{col 32}{c |} Odds Ratio{col 44}   Std. Err.{col 56}      t{col 64}   P>|t|{col 72}     [95% Con{col 85}f. Interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
equivincomeHHquintile_HBAI_imp {c |}
{space 28}2  {c |}{col 32}{res}{space 2} 1.175692{col 44}{space 2} .1914911{col 55}{space 1}    0.99{col 64}{space 3}0.320{col 72}{space 4} .8543025{col 85}{space 3} 1.617989
{txt}{space 28}3  {c |}{col 32}{res}{space 2} 1.117073{col 44}{space 2} .1750419{col 55}{space 1}    0.71{col 64}{space 3}0.480{col 72}{space 4} .8215994{col 85}{space 3} 1.518808
{txt}{space 28}4  {c |}{col 32}{res}{space 2} 1.129906{col 44}{space 2} .1677019{col 55}{space 1}    0.82{col 64}{space 3}0.411{col 72}{space 4} .8446321{col 85}{space 3} 1.511532
{txt}{space 17}Top Quintile  {c |}{col 32}{res}{space 2} 1.302247{col 44}{space 2} .1859645{col 55}{space 1}    1.85{col 64}{space 3}0.064{col 72}{space 4} .9842433{col 85}{space 3} 1.722997
{txt}{space 30} {c |}
{space 25}_cons {c |}{col 32}{res}{space 2} .2649718{col 44}{space 2} .0328694{col 55}{space 1}  -10.71{col 64}{space 3}0.000{col 72}{space 4} .2077671{col 85}{space 3} .3379266
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_worsen i.equivincomeHHquintile_HBAI_imp $controls_demographics if infullmodel_Table2 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      4.27
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.111529{col 50}{space 2} .1852479{col 61}{space 1}    0.63{col 70}{space 3}0.526{col 78}{space 4}  .801707{col 91}{space 3} 1.541082
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .9992331{col 50}{space 2} .1627154{col 61}{space 1}   -0.00{col 70}{space 3}0.996{col 78}{space 4} .7261303{col 91}{space 3} 1.375052
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.011691{col 50}{space 2} .1605068{col 61}{space 1}    0.07{col 70}{space 3}0.942{col 78}{space 4} .7412453{col 91}{space 3}  1.38081
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.081836{col 50}{space 2} .1754481{col 61}{space 1}    0.49{col 70}{space 3}0.628{col 78}{space 4} .7871807{col 91}{space 3} 1.486785
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .8927627{col 50}{space 2} .1100534{col 61}{space 1}   -0.92{col 70}{space 3}0.358{col 78}{space 4} .7010902{col 91}{space 3} 1.136837
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .7758053{col 50}{space 2}  .105277{col 61}{space 1}   -1.87{col 70}{space 3}0.061{col 78}{space 4} .5945784{col 91}{space 3}  1.01227
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}  .870817{col 50}{space 2} .0765998{col 61}{space 1}   -1.57{col 70}{space 3}0.116{col 78}{space 4} .7328747{col 91}{space 3} 1.034723
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.282467{col 50}{space 2} .1163864{col 61}{space 1}    2.74{col 70}{space 3}0.006{col 78}{space 4} 1.073431{col 91}{space 3} 1.532209
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.279242{col 50}{space 2}  .134588{col 61}{space 1}    2.34{col 70}{space 3}0.019{col 78}{space 4}  1.04081{col 91}{space 3} 1.572296
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9047129{col 50}{space 2} .0865865{col 61}{space 1}   -1.05{col 70}{space 3}0.295{col 78}{space 4}   .74993{col 91}{space 3} 1.091442
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .8892559{col 50}{space 2} .1604124{col 61}{space 1}   -0.65{col 70}{space 3}0.515{col 78}{space 4} .6243557{col 91}{space 3} 1.266548
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9881668{col 50}{space 2} .1427731{col 61}{space 1}   -0.08{col 70}{space 3}0.934{col 78}{space 4} .7444041{col 91}{space 3} 1.311752
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8492572{col 50}{space 2} .2455706{col 61}{space 1}   -0.57{col 70}{space 3}0.572{col 78}{space 4} .4817605{col 91}{space 3} 1.497088
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.059863{col 50}{space 2} .1222794{col 61}{space 1}    0.50{col 70}{space 3}0.614{col 78}{space 4} .8453065{col 91}{space 3} 1.328878
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.498164{col 50}{space 2} .1787707{col 61}{space 1}    3.39{col 70}{space 3}0.001{col 78}{space 4} 1.185652{col 91}{space 3} 1.893046
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .6336916{col 50}{space 2} .0949955{col 61}{space 1}   -3.04{col 70}{space 3}0.002{col 78}{space 4} .4723206{col 91}{space 3} .8501958
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}  .599223{col 50}{space 2} .1024432{col 61}{space 1}   -3.00{col 70}{space 3}0.003{col 78}{space 4} .4285707{col 91}{space 3} .8378272
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .3348359{col 50}{space 2} .0651311{col 61}{space 1}   -5.62{col 70}{space 3}0.000{col 78}{space 4} .2286697{col 91}{space 3} .4902927
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_worsen i.equivincomeHHquintile_HBAI_imp $controls_demographics $controls_objective if infullmodel_Table2 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      4.36
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}   1.0992{col 50}{space 2} .1850128{col 61}{space 1}    0.56{col 70}{space 3}0.574{col 78}{space 4} .7902459{col 91}{space 3} 1.528943
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.003071{col 50}{space 2} .1648735{col 61}{space 1}    0.02{col 70}{space 3}0.985{col 78}{space 4}  .726738{col 91}{space 3} 1.384476
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.023778{col 50}{space 2} .1646043{col 61}{space 1}    0.15{col 70}{space 3}0.884{col 78}{space 4} .7469762{col 91}{space 3} 1.403153
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.060449{col 50}{space 2} .1731385{col 61}{space 1}    0.36{col 70}{space 3}0.719{col 78}{space 4} .7699674{col 91}{space 3} 1.460519
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .9121425{col 50}{space 2}  .111979{col 61}{space 1}   -0.75{col 70}{space 3}0.454{col 78}{space 4} .7170231{col 91}{space 3} 1.160359
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .7963834{col 50}{space 2}  .107791{col 61}{space 1}   -1.68{col 70}{space 3}0.093{col 78}{space 4} .6107681{col 91}{space 3} 1.038408
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .8775447{col 50}{space 2} .0779128{col 61}{space 1}   -1.47{col 70}{space 3}0.141{col 78}{space 4} .7373476{col 91}{space 3} 1.044399
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  1.26827{col 50}{space 2}  .116173{col 61}{space 1}    2.59{col 70}{space 3}0.010{col 78}{space 4} 1.059786{col 91}{space 3} 1.517768
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.270618{col 50}{space 2} .1350353{col 61}{space 1}    2.25{col 70}{space 3}0.024{col 78}{space 4} 1.031634{col 91}{space 3} 1.564964
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9688247{col 50}{space 2} .0945639{col 61}{space 1}   -0.32{col 70}{space 3}0.746{col 78}{space 4} .8000861{col 91}{space 3}  1.17315
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9331544{col 50}{space 2} .1706257{col 61}{space 1}   -0.38{col 70}{space 3}0.705{col 78}{space 4} .6520264{col 91}{space 3} 1.335494
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9994992{col 50}{space 2} .1454519{col 61}{space 1}   -0.00{col 70}{space 3}0.997{col 78}{space 4} .7514043{col 91}{space 3} 1.329509
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8543309{col 50}{space 2} .2446505{col 61}{space 1}   -0.55{col 70}{space 3}0.583{col 78}{space 4} .4873009{col 91}{space 3} 1.497804
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}   .94564{col 50}{space 2} .1172553{col 61}{space 1}   -0.45{col 70}{space 3}0.652{col 78}{space 4} .7415634{col 91}{space 3} 1.205878
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.000113{col 50}{space 2} .1883523{col 61}{space 1}    0.00{col 70}{space 3}1.000{col 78}{space 4} .6913415{col 91}{space 3} 1.446791
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .7392922{col 50}{space 2} .1278909{col 61}{space 1}   -1.75{col 70}{space 3}0.081{col 78}{space 4} .5266486{col 91}{space 3} 1.037794
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .8875048{col 50}{space 2} .2078441{col 61}{space 1}   -0.51{col 70}{space 3}0.610{col 78}{space 4} .5607477{col 91}{space 3} 1.404669
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.132371{col 50}{space 2} .1272744{col 61}{space 1}    1.11{col 70}{space 3}0.269{col 78}{space 4} .9084218{col 91}{space 3} 1.411529
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.293345{col 50}{space 2} .3083182{col 61}{space 1}    1.08{col 70}{space 3}0.281{col 78}{space 4} .8104681{col 91}{space 3}  2.06392
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.012834{col 50}{space 2} .2390897{col 61}{space 1}    0.05{col 70}{space 3}0.957{col 78}{space 4} .6375913{col 91}{space 3}  1.60892
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2}  1.47375{col 50}{space 2} .3200393{col 61}{space 1}    1.79{col 70}{space 3}0.074{col 78}{space 4} .9627655{col 91}{space 3} 2.255938
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .8816561{col 50}{space 2}   .12698{col 61}{space 1}   -0.87{col 70}{space 3}0.382{col 78}{space 4} .6647648{col 91}{space 3} 1.169312
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.180469{col 50}{space 2} .1459234{col 61}{space 1}    1.34{col 70}{space 3}0.180{col 78}{space 4} .9264062{col 91}{space 3} 1.504208
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2431594{col 50}{space 2} .0630194{col 61}{space 1}   -5.46{col 70}{space 3}0.000{col 78}{space 4} .1462913{col 91}{space 3} .4041697
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 2: Improve 
. svy: logit AI_improve i.equivincomeHHquintile_HBAI_imp if infullmodel_Table2 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   4{txt},{res}   3959{txt}){col 67}= {res}     11.26
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 32}{c |}{col 44}  Linearized
{col 1}                    AI_improve{col 32}{c |} Odds Ratio{col 44}   Std. Err.{col 56}      t{col 64}   P>|t|{col 72}     [95% Con{col 85}f. Interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
equivincomeHHquintile_HBAI_imp {c |}
{space 28}2  {c |}{col 32}{res}{space 2} .5627264{col 44}{space 2} .1606201{col 55}{space 1}   -2.01{col 64}{space 3}0.044{col 72}{space 4} .3215607{col 85}{space 3} .9847629
{txt}{space 28}3  {c |}{col 32}{res}{space 2} .6483372{col 44}{space 2} .1733206{col 55}{space 1}   -1.62{col 64}{space 3}0.105{col 72}{space 4} .3838654{col 85}{space 3} 1.095022
{txt}{space 28}4  {c |}{col 32}{res}{space 2} .8170951{col 44}{space 2} .1900892{col 55}{space 1}   -0.87{col 64}{space 3}0.385{col 72}{space 4} .5178314{col 85}{space 3} 1.289309
{txt}{space 17}Top Quintile  {c |}{col 32}{res}{space 2} 1.627773{col 44}{space 2} .3463527{col 55}{space 1}    2.29{col 64}{space 3}0.022{col 72}{space 4} 1.072561{col 85}{space 3} 2.470389
{txt}{space 30} {c |}
{space 25}_cons {c |}{col 32}{res}{space 2} .1040184{col 44}{space 2} .0202437{col 55}{space 1}  -11.63{col 64}{space 3}0.000{col 72}{space 4} .0710235{col 85}{space 3} .1523417
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_improve i.equivincomeHHquintile_HBAI_imp $controls_demographics if infullmodel_Table2 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      9.00
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                          AI_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5114603{col 50}{space 2} .1552909{col 61}{space 1}   -2.21{col 70}{space 3}0.027{col 78}{space 4} .2820259{col 91}{space 3} .9275449
{txt}{space 34}3  {c |}{col 38}{res}{space 2}  .431203{col 50}{space 2} .1200384{col 61}{space 1}   -3.02{col 70}{space 3}0.003{col 78}{space 4} .2498341{col 91}{space 3} .7442382
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .4891384{col 50}{space 2} .1216417{col 61}{space 1}   -2.88{col 70}{space 3}0.004{col 78}{space 4}   .30039{col 91}{space 3} .7964859
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .6643646{col 50}{space 2} .1598227{col 61}{space 1}   -1.70{col 70}{space 3}0.089{col 78}{space 4} .4145481{col 91}{space 3} 1.064727
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .5859237{col 50}{space 2} .0967461{col 61}{space 1}   -3.24{col 70}{space 3}0.001{col 78}{space 4} .4238869{col 91}{space 3} .8099013
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .4313816{col 50}{space 2} .0848871{col 61}{space 1}   -4.27{col 70}{space 3}0.000{col 78}{space 4} .2932994{col 91}{space 3} .6344712
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6274358{col 50}{space 2} .0815649{col 61}{space 1}   -3.59{col 70}{space 3}0.000{col 78}{space 4} .4862747{col 91}{space 3} .8095748
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.753261{col 50}{space 2} .2386836{col 61}{space 1}    4.12{col 70}{space 3}0.000{col 78}{space 4} 1.342552{col 91}{space 3} 2.289613
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .4766213{col 50}{space 2} .0975186{col 61}{space 1}   -3.62{col 70}{space 3}0.000{col 78}{space 4} .3191252{col 91}{space 3} .7118456
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2}  .703992{col 50}{space 2} .1022251{col 61}{space 1}   -2.42{col 70}{space 3}0.016{col 78}{space 4} .5295767{col 91}{space 3} .9358507
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .6980172{col 50}{space 2} .1996649{col 61}{space 1}   -1.26{col 70}{space 3}0.209{col 78}{space 4} .3983906{col 91}{space 3} 1.222991
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.045743{col 50}{space 2} .2381017{col 61}{space 1}    0.20{col 70}{space 3}0.844{col 78}{space 4} .6692037{col 91}{space 3} 1.634148
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.253748{col 50}{space 2} .6701505{col 61}{space 1}    0.42{col 70}{space 3}0.672{col 78}{space 4} .4396317{col 91}{space 3} 3.575457
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .8602597{col 50}{space 2} .1431378{col 61}{space 1}   -0.90{col 70}{space 3}0.366{col 78}{space 4} .6208056{col 91}{space 3} 1.192075
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}  .483637{col 50}{space 2} .1091257{col 61}{space 1}   -3.22{col 70}{space 3}0.001{col 78}{space 4} .3107417{col 91}{space 3} .7527305
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .4243676{col 50}{space 2} .1168096{col 61}{space 1}   -3.11{col 70}{space 3}0.002{col 78}{space 4} .2473845{col 91}{space 3} .7279674
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .3840638{col 50}{space 2} .1048289{col 61}{space 1}   -3.51{col 70}{space 3}0.000{col 78}{space 4} .2249053{col 91}{space 3} .6558537
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .4719797{col 50}{space 2} .1441441{col 61}{space 1}   -2.46{col 70}{space 3}0.014{col 78}{space 4} .2593487{col 91}{space 3} .8589393
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_improve i.equivincomeHHquintile_HBAI_imp $controls_demographics $controls_objective if infullmodel_Table2 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      7.85
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                          AI_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5083028{col 50}{space 2} .1523285{col 61}{space 1}   -2.26{col 70}{space 3}0.024{col 78}{space 4} .2824594{col 91}{space 3}  .914722
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .4113217{col 50}{space 2} .1135379{col 61}{space 1}   -3.22{col 70}{space 3}0.001{col 78}{space 4} .2394149{col 91}{space 3} .7066626
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .4648291{col 50}{space 2} .1149079{col 61}{space 1}   -3.10{col 70}{space 3}0.002{col 78}{space 4} .2862912{col 91}{space 3} .7547074
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .6062726{col 50}{space 2} .1449004{col 61}{space 1}   -2.09{col 70}{space 3}0.036{col 78}{space 4} .3794607{col 91}{space 3} .9686549
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .5967726{col 50}{space 2} .0987241{col 61}{space 1}   -3.12{col 70}{space 3}0.002{col 78}{space 4}  .431471{col 91}{space 3} .8254034
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .4389943{col 50}{space 2} .0868928{col 61}{space 1}   -4.16{col 70}{space 3}0.000{col 78}{space 4} .2977995{col 91}{space 3} .6471334
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6317683{col 50}{space 2} .0833817{col 61}{space 1}   -3.48{col 70}{space 3}0.001{col 78}{space 4} .4877314{col 91}{space 3} .8183422
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.732196{col 50}{space 2} .2368375{col 61}{space 1}    4.02{col 70}{space 3}0.000{col 78}{space 4} 1.324889{col 91}{space 3} 2.264721
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .4842575{col 50}{space 2} .0992227{col 61}{space 1}   -3.54{col 70}{space 3}0.000{col 78}{space 4} .3240521{col 91}{space 3} .7236656
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .6974604{col 50}{space 2} .1028677{col 61}{space 1}   -2.44{col 70}{space 3}0.015{col 78}{space 4} .5223221{col 91}{space 3} .9313239
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .6836831{col 50}{space 2} .1929683{col 61}{space 1}   -1.35{col 70}{space 3}0.178{col 78}{space 4} .3931256{col 91}{space 3}  1.18899
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.129698{col 50}{space 2} .2598135{col 61}{space 1}    0.53{col 70}{space 3}0.596{col 78}{space 4} .7196789{col 91}{space 3} 1.773314
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.339877{col 50}{space 2} .7204718{col 61}{space 1}    0.54{col 70}{space 3}0.586{col 78}{space 4} .4668973{col 91}{space 3} 3.845108
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.010877{col 50}{space 2} .1838329{col 61}{space 1}    0.06{col 70}{space 3}0.953{col 78}{space 4} .7077112{col 91}{space 3} 1.443913
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.090424{col 50}{space 2} .3315547{col 61}{space 1}    0.28{col 70}{space 3}0.776{col 78}{space 4} .6007582{col 91}{space 3} 1.979207
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .7050556{col 50}{space 2} .2294216{col 61}{space 1}   -1.07{col 70}{space 3}0.283{col 78}{space 4} .3725306{col 91}{space 3} 1.334396
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}  .859264{col 50}{space 2} .2912932{col 61}{space 1}   -0.45{col 70}{space 3}0.655{col 78}{space 4} .4420572{col 91}{space 3} 1.670224
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.600942{col 50}{space 2} .2810704{col 61}{space 1}    2.68{col 70}{space 3}0.007{col 78}{space 4} 1.134718{col 91}{space 3} 2.258723
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.005969{col 50}{space 2} .3909627{col 61}{space 1}    0.02{col 70}{space 3}0.988{col 78}{space 4} .4695373{col 91}{space 3} 2.155257
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2}  1.09556{col 50}{space 2} .4040863{col 61}{space 1}    0.25{col 70}{space 3}0.805{col 78}{space 4} .5315977{col 91}{space 3}  2.25782
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .3170697{col 50}{space 2}  .116649{col 61}{space 1}   -3.12{col 70}{space 3}0.002{col 78}{space 4} .1541361{col 91}{space 3} .6522364
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.003973{col 50}{space 2} .2001391{col 61}{space 1}    0.02{col 70}{space 3}0.984{col 78}{space 4} .6791814{col 91}{space 3} 1.484084
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2}   .88363{col 50}{space 2} .1492986{col 61}{space 1}   -0.73{col 70}{space 3}0.464{col 78}{space 4}  .634464{col 91}{space 3} 1.230648
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .3058212{col 50}{space 2} .1182217{col 61}{space 1}   -3.06{col 70}{space 3}0.002{col 78}{space 4} .1433233{col 91}{space 3} .6525565
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 3: Worsen v Improve
. svy: logit AI_directionworse i.equivincomeHHquintile_HBAI_imp if infullmodel_Table2 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}   4{txt},{res}   1318{txt}){col 67}= {res}      5.61
{txt}{col 49}Prob > F{col 67}= {res}    0.0002

{txt}{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 32}{c |}{col 44}  Linearized
{col 1}             AI_directionworse{col 32}{c |} Odds Ratio{col 44}   Std. Err.{col 56}      t{col 64}   P>|t|{col 72}     [95% Con{col 85}f. Interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
equivincomeHHquintile_HBAI_imp {c |}
{space 28}2  {c |}{col 32}{res}{space 2} 1.932096{col 44}{space 2} .6015029{col 55}{space 1}    2.12{col 64}{space 3}0.035{col 72}{space 4} 1.049031{col 85}{space 3} 3.558517
{txt}{space 28}3  {c |}{col 32}{res}{space 2} 1.626019{col 44}{space 2} .4759396{col 55}{space 1}    1.66{col 64}{space 3}0.097{col 72}{space 4} .9156899{col 85}{space 3} 2.887372
{txt}{space 28}4  {c |}{col 32}{res}{space 2} 1.323002{col 44}{space 2} .3419835{col 55}{space 1}    1.08{col 64}{space 3}0.279{col 72}{space 4}  .796767{col 85}{space 3} 2.196797
{txt}{space 17}Top Quintile  {c |}{col 32}{res}{space 2} .7968853{col 44}{space 2} .1901129{col 55}{space 1}   -0.95{col 64}{space 3}0.341{col 72}{space 4} .4990436{col 85}{space 3} 1.272486
{txt}{space 30} {c |}
{space 25}_cons {c |}{col 32}{res}{space 2} 2.223232{col 44}{space 2} .4814285{col 55}{space 1}    3.69{col 64}{space 3}0.000{col 72}{space 4}  1.45376{col 85}{space 3} 3.399983
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_directionworse i.equivincomeHHquintile_HBAI_imp $controls_demographics if infullmodel_Table2 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}  17{txt},{res}   1305{txt}){col 67}= {res}      4.64
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                   AI_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.921637{col 50}{space 2} .6822025{col 61}{space 1}    1.84{col 70}{space 3}0.066{col 78}{space 4} .9576527{col 91}{space 3} 3.855977
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 2.194025{col 50}{space 2} .7060782{col 61}{space 1}    2.44{col 70}{space 3}0.015{col 78}{space 4} 1.166965{col 91}{space 3} 4.125013
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.954344{col 50}{space 2} .5733307{col 61}{space 1}    2.28{col 70}{space 3}0.023{col 78}{space 4} 1.099161{col 91}{space 3} 3.474884
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.511639{col 50}{space 2} .4308267{col 61}{space 1}    1.45{col 70}{space 3}0.147{col 78}{space 4} .8642265{col 91}{space 3} 2.644042
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.630793{col 50}{space 2} .3050056{col 61}{space 1}    2.61{col 70}{space 3}0.009{col 78}{space 4} 1.129935{col 91}{space 3} 2.353663
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.731111{col 50}{space 2} .3690646{col 61}{space 1}    2.57{col 70}{space 3}0.010{col 78}{space 4} 1.139427{col 91}{space 3} 2.630047
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} 1.343212{col 50}{space 2} .1961699{col 61}{space 1}    2.02{col 70}{space 3}0.044{col 78}{space 4} 1.008595{col 91}{space 3} 1.788845
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7325523{col 50}{space 2} .1120903{col 61}{space 1}   -2.03{col 70}{space 3}0.042{col 78}{space 4} .5425927{col 91}{space 3}  .989016
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 2.387622{col 50}{space 2} .5475305{col 61}{space 1}    3.80{col 70}{space 3}0.000{col 78}{space 4}  1.52261{col 91}{space 3} 3.744057
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.240947{col 50}{space 2} .2031517{col 61}{space 1}    1.32{col 70}{space 3}0.188{col 78}{space 4} .9000731{col 91}{space 3} 1.710916
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}  1.22502{col 50}{space 2} .4068413{col 61}{space 1}    0.61{col 70}{space 3}0.541{col 78}{space 4} .6385446{col 91}{space 3} 2.350146
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.053222{col 50}{space 2} .2908356{col 61}{space 1}    0.19{col 70}{space 3}0.851{col 78}{space 4} .6127091{col 91}{space 3} 1.810445
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .6364657{col 50}{space 2}  .396653{col 61}{space 1}   -0.72{col 70}{space 3}0.469{col 78}{space 4} .1874172{col 91}{space 3} 2.161426
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.146499{col 50}{space 2} .2142211{col 61}{space 1}    0.73{col 70}{space 3}0.464{col 78}{space 4} .7946625{col 91}{space 3} 1.654112
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 2.509639{col 50}{space 2} .6072188{col 61}{space 1}    3.80{col 70}{space 3}0.000{col 78}{space 4} 1.561242{col 91}{space 3} 4.034152
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.649087{col 50}{space 2} .5168764{col 61}{space 1}    1.60{col 70}{space 3}0.111{col 78}{space 4}  .891672{col 91}{space 3} 3.049874
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.619572{col 50}{space 2} .4988888{col 61}{space 1}    1.57{col 70}{space 3}0.118{col 78}{space 4} .8850298{col 91}{space 3} 2.963757
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .6692618{col 50}{space 2} .2259274{col 61}{space 1}   -1.19{col 70}{space 3}0.234{col 78}{space 4}  .345133{col 91}{space 3} 1.297794
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_directionworse i.equivincomeHHquintile_HBAI_imp $controls_demographics $controls_objective if infullmodel_Table2 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}  23{txt},{res}   1299{txt}){col 67}= {res}      4.25
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                   AI_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.823815{col 50}{space 2} .6373137{col 61}{space 1}    1.72{col 70}{space 3}0.086{col 78}{space 4} .9188914{col 91}{space 3} 3.619906
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 2.220856{col 50}{space 2} .7064391{col 61}{space 1}    2.51{col 70}{space 3}0.012{col 78}{space 4} 1.189901{col 91}{space 3} 4.145053
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.988575{col 50}{space 2} .5775212{col 61}{space 1}    2.37{col 70}{space 3}0.018{col 78}{space 4} 1.124889{col 91}{space 3} 3.515397
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.569265{col 50}{space 2} .4418033{col 61}{space 1}    1.60{col 70}{space 3}0.110{col 78}{space 4} .9033028{col 91}{space 3}  2.72621
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.643828{col 50}{space 2} .3076717{col 61}{space 1}    2.66{col 70}{space 3}0.008{col 78}{space 4} 1.138657{col 91}{space 3} 2.373122
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.718449{col 50}{space 2} .3686662{col 61}{space 1}    2.52{col 70}{space 3}0.012{col 78}{space 4} 1.128125{col 91}{space 3} 2.617677
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} 1.335884{col 50}{space 2} .1975336{col 61}{space 1}    1.96{col 70}{space 3}0.050{col 78}{space 4} .9995127{col 91}{space 3} 1.785456
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7356201{col 50}{space 2} .1132835{col 61}{space 1}   -1.99{col 70}{space 3}0.046{col 78}{space 4} .5438144{col 91}{space 3} .9950766
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 2.412895{col 50}{space 2} .5516282{col 61}{space 1}    3.85{col 70}{space 3}0.000{col 78}{space 4} 1.540852{col 91}{space 3} 3.778468
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.315665{col 50}{space 2} .2214155{col 61}{space 1}    1.63{col 70}{space 3}0.103{col 78}{space 4} .9457228{col 91}{space 3} 1.830319
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.318259{col 50}{space 2} .4386386{col 61}{space 1}    0.83{col 70}{space 3}0.406{col 78}{space 4}  .686296{col 91}{space 3} 2.532153
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2}  .926384{col 50}{space 2} .2584575{col 61}{space 1}   -0.27{col 70}{space 3}0.784{col 78}{space 4} .5359093{col 91}{space 3} 1.601367
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5966104{col 50}{space 2} .3877683{col 61}{space 1}   -0.79{col 70}{space 3}0.427{col 78}{space 4} .1667029{col 91}{space 3}   2.1352
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}  .926379{col 50}{space 2} .1941269{col 61}{space 1}   -0.36{col 70}{space 3}0.715{col 78}{space 4} .6141183{col 91}{space 3} 1.397415
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} .8738151{col 50}{space 2} .3061096{col 61}{space 1}   -0.39{col 70}{space 3}0.700{col 78}{space 4} .4394994{col 91}{space 3} 1.737324
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.107186{col 50}{space 2} .4118154{col 61}{space 1}    0.27{col 70}{space 3}0.784{col 78}{space 4} .5337372{col 91}{space 3} 2.296748
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .9733152{col 50}{space 2}   .39149{col 61}{space 1}   -0.07{col 70}{space 3}0.946{col 78}{space 4} .4421467{col 91}{space 3} 2.142597
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .7403207{col 50}{space 2} .1530683{col 61}{space 1}   -1.45{col 70}{space 3}0.146{col 78}{space 4} .4934744{col 91}{space 3} 1.110645
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.080125{col 50}{space 2} .4675268{col 61}{space 1}    0.18{col 70}{space 3}0.859{col 78}{space 4} .4620594{col 91}{space 3} 2.524936
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} .8527179{col 50}{space 2} .3655586{col 61}{space 1}   -0.37{col 70}{space 3}0.710{col 78}{space 4} .3677581{col 91}{space 3} 1.977191
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} 3.897365{col 50}{space 2}  1.55288{col 61}{space 1}    3.41{col 70}{space 3}0.001{col 78}{space 4} 1.783628{col 91}{space 3} 8.516043
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .9586039{col 50}{space 2} .2224665{col 61}{space 1}   -0.18{col 70}{space 3}0.855{col 78}{space 4} .6080186{col 91}{space 3} 1.511338
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.227994{col 50}{space 2} .2313644{col 61}{space 1}    1.09{col 70}{space 3}0.276{col 78}{space 4} .8485466{col 91}{space 3}  1.77712
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .8664103{col 50}{space 2}  .380473{col 61}{space 1}   -0.33{col 70}{space 3}0.744{col 78}{space 4} .3660889{col 91}{space 3} 2.050504
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. ******* Employment Status ********
. 
. 
. * Model 1: Worsen 
. svy: logit AI_worsen ib2.employmentstatus5 if infullmodel_Table2 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   1{txt},{res}   3962{txt}){col 67}= {res}      0.65
{txt}{col 49}Prob > F{col 67}= {res}    0.4196

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}  Linearized
{col 1}        AI_worsen{col 19}{c |} Odds Ratio{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
employmentstatus5 {c |}
{space 7}FT Worker  {c |}{col 19}{res}{space 2} .9283357{col 31}{space 2} .0855293{col 42}{space 1}   -0.81{col 51}{space 3}0.420{col 59}{space 4} .7749226{col 72}{space 3}  1.11212
{txt}{space 12}_cons {c |}{col 19}{res}{space 2}   .32743{col 31}{space 2} .0259688{col 42}{space 1}  -14.08{col 51}{space 3}0.000{col 59}{space 4} .2802774{col 72}{space 3} .3825152
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_worsen ib2.employmentstatus5 $controls_demographics if infullmodel_Table2 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      4.27
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} .7817127{col 50}{space 2} .0822433{col 61}{space 1}   -2.34{col 70}{space 3}0.019{col 78}{space 4} .6360124{col 91}{space 3} .9607906
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .8927627{col 50}{space 2} .1100534{col 61}{space 1}   -0.92{col 70}{space 3}0.358{col 78}{space 4} .7010902{col 91}{space 3} 1.136837
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .7758053{col 50}{space 2}  .105277{col 61}{space 1}   -1.87{col 70}{space 3}0.061{col 78}{space 4} .5945784{col 91}{space 3}  1.01227
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}  .870817{col 50}{space 2} .0765998{col 61}{space 1}   -1.57{col 70}{space 3}0.116{col 78}{space 4} .7328747{col 91}{space 3} 1.034723
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.282467{col 50}{space 2} .1163864{col 61}{space 1}    2.74{col 70}{space 3}0.006{col 78}{space 4} 1.073431{col 91}{space 3} 1.532209
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9047129{col 50}{space 2} .0865865{col 61}{space 1}   -1.05{col 70}{space 3}0.295{col 78}{space 4}   .74993{col 91}{space 3} 1.091442
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .8892559{col 50}{space 2} .1604124{col 61}{space 1}   -0.65{col 70}{space 3}0.515{col 78}{space 4} .6243557{col 91}{space 3} 1.266548
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9881668{col 50}{space 2} .1427731{col 61}{space 1}   -0.08{col 70}{space 3}0.934{col 78}{space 4} .7444041{col 91}{space 3} 1.311752
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8492572{col 50}{space 2} .2455706{col 61}{space 1}   -0.57{col 70}{space 3}0.572{col 78}{space 4} .4817605{col 91}{space 3} 1.497088
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.111529{col 50}{space 2} .1852479{col 61}{space 1}    0.63{col 70}{space 3}0.526{col 78}{space 4}  .801707{col 91}{space 3} 1.541082
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .9992331{col 50}{space 2} .1627154{col 61}{space 1}   -0.00{col 70}{space 3}0.996{col 78}{space 4} .7261303{col 91}{space 3} 1.375052
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.011691{col 50}{space 2} .1605068{col 61}{space 1}    0.07{col 70}{space 3}0.942{col 78}{space 4} .7412453{col 91}{space 3}  1.38081
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.081836{col 50}{space 2} .1754481{col 61}{space 1}    0.49{col 70}{space 3}0.628{col 78}{space 4} .7871807{col 91}{space 3} 1.486785
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.059863{col 50}{space 2} .1222794{col 61}{space 1}    0.50{col 70}{space 3}0.614{col 78}{space 4} .8453065{col 91}{space 3} 1.328878
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.498164{col 50}{space 2} .1787707{col 61}{space 1}    3.39{col 70}{space 3}0.001{col 78}{space 4} 1.185652{col 91}{space 3} 1.893046
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .6336916{col 50}{space 2} .0949955{col 61}{space 1}   -3.04{col 70}{space 3}0.002{col 78}{space 4} .4723206{col 91}{space 3} .8501958
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}  .599223{col 50}{space 2} .1024432{col 61}{space 1}   -3.00{col 70}{space 3}0.003{col 78}{space 4} .4285707{col 91}{space 3} .8378272
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .4283363{col 50}{space 2}  .091154{col 61}{space 1}   -3.98{col 70}{space 3}0.000{col 78}{space 4} .2822188{col 91}{space 3} .6501055
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_worsen ib2.employmentstatus5 $controls_demographics $controls_objective if infullmodel_Table2 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      4.36
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} .7870184{col 50}{space 2} .0836406{col 61}{space 1}   -2.25{col 70}{space 3}0.024{col 78}{space 4} .6389923{col 91}{space 3} .9693357
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .9121425{col 50}{space 2}  .111979{col 61}{space 1}   -0.75{col 70}{space 3}0.454{col 78}{space 4} .7170231{col 91}{space 3} 1.160359
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .7963834{col 50}{space 2}  .107791{col 61}{space 1}   -1.68{col 70}{space 3}0.093{col 78}{space 4} .6107681{col 91}{space 3} 1.038408
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .8775447{col 50}{space 2} .0779128{col 61}{space 1}   -1.47{col 70}{space 3}0.141{col 78}{space 4} .7373476{col 91}{space 3} 1.044399
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  1.26827{col 50}{space 2}  .116173{col 61}{space 1}    2.59{col 70}{space 3}0.010{col 78}{space 4} 1.059786{col 91}{space 3} 1.517768
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9688247{col 50}{space 2} .0945639{col 61}{space 1}   -0.32{col 70}{space 3}0.746{col 78}{space 4} .8000861{col 91}{space 3}  1.17315
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9331544{col 50}{space 2} .1706257{col 61}{space 1}   -0.38{col 70}{space 3}0.705{col 78}{space 4} .6520264{col 91}{space 3} 1.335494
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9994992{col 50}{space 2} .1454519{col 61}{space 1}   -0.00{col 70}{space 3}0.997{col 78}{space 4} .7514043{col 91}{space 3} 1.329509
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8543309{col 50}{space 2} .2446505{col 61}{space 1}   -0.55{col 70}{space 3}0.583{col 78}{space 4} .4873009{col 91}{space 3} 1.497804
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}   1.0992{col 50}{space 2} .1850128{col 61}{space 1}    0.56{col 70}{space 3}0.574{col 78}{space 4} .7902459{col 91}{space 3} 1.528943
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.003071{col 50}{space 2} .1648735{col 61}{space 1}    0.02{col 70}{space 3}0.985{col 78}{space 4}  .726738{col 91}{space 3} 1.384476
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.023778{col 50}{space 2} .1646043{col 61}{space 1}    0.15{col 70}{space 3}0.884{col 78}{space 4} .7469762{col 91}{space 3} 1.403153
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.060449{col 50}{space 2} .1731385{col 61}{space 1}    0.36{col 70}{space 3}0.719{col 78}{space 4} .7699674{col 91}{space 3} 1.460519
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}   .94564{col 50}{space 2} .1172553{col 61}{space 1}   -0.45{col 70}{space 3}0.652{col 78}{space 4} .7415634{col 91}{space 3} 1.205878
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.000113{col 50}{space 2} .1883523{col 61}{space 1}    0.00{col 70}{space 3}1.000{col 78}{space 4} .6913415{col 91}{space 3} 1.446791
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .7392922{col 50}{space 2} .1278909{col 61}{space 1}   -1.75{col 70}{space 3}0.081{col 78}{space 4} .5266486{col 91}{space 3} 1.037794
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .8875048{col 50}{space 2} .2078441{col 61}{space 1}   -0.51{col 70}{space 3}0.610{col 78}{space 4} .5607477{col 91}{space 3} 1.404669
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.132371{col 50}{space 2} .1272744{col 61}{space 1}    1.11{col 70}{space 3}0.269{col 78}{space 4} .9084218{col 91}{space 3} 1.411529
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.293345{col 50}{space 2} .3083182{col 61}{space 1}    1.08{col 70}{space 3}0.281{col 78}{space 4} .8104681{col 91}{space 3}  2.06392
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.012834{col 50}{space 2} .2390897{col 61}{space 1}    0.05{col 70}{space 3}0.957{col 78}{space 4} .6375913{col 91}{space 3}  1.60892
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2}  1.47375{col 50}{space 2} .3200393{col 61}{space 1}    1.79{col 70}{space 3}0.074{col 78}{space 4} .9627655{col 91}{space 3} 2.255938
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .8816561{col 50}{space 2}   .12698{col 61}{space 1}   -0.87{col 70}{space 3}0.382{col 78}{space 4} .6647648{col 91}{space 3} 1.169312
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.180469{col 50}{space 2} .1459234{col 61}{space 1}    1.34{col 70}{space 3}0.180{col 78}{space 4} .9264062{col 91}{space 3} 1.504208
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .3089628{col 50}{space 2}  .086092{col 61}{space 1}   -4.22{col 70}{space 3}0.000{col 78}{space 4} .1789155{col 91}{space 3}  .533537
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 2: Improve 
. svy: logit AI_improve ib2.employmentstatus5 if infullmodel_Table2 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   1{txt},{res}   3962{txt}){col 67}= {res}     32.56
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}  Linearized
{col 1}       AI_improve{col 19}{c |} Odds Ratio{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
employmentstatus5 {c |}
{space 7}FT Worker  {c |}{col 19}{res}{space 2} 3.037998{col 31}{space 2}  .591625{col 42}{space 1}    5.71{col 51}{space 3}0.000{col 59}{space 4} 2.073826{col 72}{space 3} 4.450437
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} .0412497{col 31}{space 2} .0075857{col 42}{space 1}  -17.34{col 51}{space 3}0.000{col 59}{space 4} .0287634{col 72}{space 3} .0591563
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_improve ib2.employmentstatus5 $controls_demographics if infullmodel_Table2 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      9.00
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                          AI_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} 2.098102{col 50}{space 2} .4292798{col 61}{space 1}    3.62{col 70}{space 3}0.000{col 78}{space 4} 1.404799{col 91}{space 3} 3.133567
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .5859237{col 50}{space 2} .0967461{col 61}{space 1}   -3.24{col 70}{space 3}0.001{col 78}{space 4} .4238869{col 91}{space 3} .8099013
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .4313816{col 50}{space 2} .0848871{col 61}{space 1}   -4.27{col 70}{space 3}0.000{col 78}{space 4} .2932994{col 91}{space 3} .6344712
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6274358{col 50}{space 2} .0815649{col 61}{space 1}   -3.59{col 70}{space 3}0.000{col 78}{space 4} .4862747{col 91}{space 3} .8095748
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.753261{col 50}{space 2} .2386836{col 61}{space 1}    4.12{col 70}{space 3}0.000{col 78}{space 4} 1.342552{col 91}{space 3} 2.289613
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2}  .703992{col 50}{space 2} .1022251{col 61}{space 1}   -2.42{col 70}{space 3}0.016{col 78}{space 4} .5295767{col 91}{space 3} .9358507
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .6980172{col 50}{space 2} .1996649{col 61}{space 1}   -1.26{col 70}{space 3}0.209{col 78}{space 4} .3983906{col 91}{space 3} 1.222991
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.045743{col 50}{space 2} .2381017{col 61}{space 1}    0.20{col 70}{space 3}0.844{col 78}{space 4} .6692037{col 91}{space 3} 1.634148
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.253748{col 50}{space 2} .6701505{col 61}{space 1}    0.42{col 70}{space 3}0.672{col 78}{space 4} .4396317{col 91}{space 3} 3.575457
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5114603{col 50}{space 2} .1552909{col 61}{space 1}   -2.21{col 70}{space 3}0.027{col 78}{space 4} .2820259{col 91}{space 3} .9275449
{txt}{space 34}3  {c |}{col 38}{res}{space 2}  .431203{col 50}{space 2} .1200384{col 61}{space 1}   -3.02{col 70}{space 3}0.003{col 78}{space 4} .2498341{col 91}{space 3} .7442382
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .4891384{col 50}{space 2} .1216417{col 61}{space 1}   -2.88{col 70}{space 3}0.004{col 78}{space 4}   .30039{col 91}{space 3} .7964859
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .6643646{col 50}{space 2} .1598227{col 61}{space 1}   -1.70{col 70}{space 3}0.089{col 78}{space 4} .4145481{col 91}{space 3} 1.064727
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .8602597{col 50}{space 2} .1431378{col 61}{space 1}   -0.90{col 70}{space 3}0.366{col 78}{space 4} .6208056{col 91}{space 3} 1.192075
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}  .483637{col 50}{space 2} .1091257{col 61}{space 1}   -3.22{col 70}{space 3}0.001{col 78}{space 4} .3107417{col 91}{space 3} .7527305
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .4243676{col 50}{space 2} .1168096{col 61}{space 1}   -3.11{col 70}{space 3}0.002{col 78}{space 4} .2473845{col 91}{space 3} .7279674
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .3840638{col 50}{space 2} .1048289{col 61}{space 1}   -3.51{col 70}{space 3}0.000{col 78}{space 4} .2249053{col 91}{space 3} .6558537
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2249556{col 50}{space 2} .0741438{col 61}{space 1}   -4.53{col 70}{space 3}0.000{col 78}{space 4} .1178857{col 91}{space 3} .4292719
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_improve ib2.employmentstatus5 $controls_demographics $controls_objective if infullmodel_Table2 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      7.85
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                          AI_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} 2.065017{col 50}{space 2} .4231148{col 61}{space 1}    3.54{col 70}{space 3}0.000{col 78}{space 4} 1.381854{col 91}{space 3} 3.085924
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .5967726{col 50}{space 2} .0987241{col 61}{space 1}   -3.12{col 70}{space 3}0.002{col 78}{space 4}  .431471{col 91}{space 3} .8254034
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .4389943{col 50}{space 2} .0868928{col 61}{space 1}   -4.16{col 70}{space 3}0.000{col 78}{space 4} .2977995{col 91}{space 3} .6471334
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6317683{col 50}{space 2} .0833817{col 61}{space 1}   -3.48{col 70}{space 3}0.001{col 78}{space 4} .4877314{col 91}{space 3} .8183422
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.732196{col 50}{space 2} .2368375{col 61}{space 1}    4.02{col 70}{space 3}0.000{col 78}{space 4} 1.324889{col 91}{space 3} 2.264721
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .6974604{col 50}{space 2} .1028677{col 61}{space 1}   -2.44{col 70}{space 3}0.015{col 78}{space 4} .5223221{col 91}{space 3} .9313239
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .6836831{col 50}{space 2} .1929683{col 61}{space 1}   -1.35{col 70}{space 3}0.178{col 78}{space 4} .3931256{col 91}{space 3}  1.18899
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.129698{col 50}{space 2} .2598135{col 61}{space 1}    0.53{col 70}{space 3}0.596{col 78}{space 4} .7196789{col 91}{space 3} 1.773314
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.339877{col 50}{space 2} .7204718{col 61}{space 1}    0.54{col 70}{space 3}0.586{col 78}{space 4} .4668973{col 91}{space 3} 3.845108
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5083028{col 50}{space 2} .1523285{col 61}{space 1}   -2.26{col 70}{space 3}0.024{col 78}{space 4} .2824594{col 91}{space 3}  .914722
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .4113217{col 50}{space 2} .1135379{col 61}{space 1}   -3.22{col 70}{space 3}0.001{col 78}{space 4} .2394149{col 91}{space 3} .7066626
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .4648291{col 50}{space 2} .1149079{col 61}{space 1}   -3.10{col 70}{space 3}0.002{col 78}{space 4} .2862912{col 91}{space 3} .7547074
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .6062726{col 50}{space 2} .1449004{col 61}{space 1}   -2.09{col 70}{space 3}0.036{col 78}{space 4} .3794607{col 91}{space 3} .9686549
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.010877{col 50}{space 2} .1838329{col 61}{space 1}    0.06{col 70}{space 3}0.953{col 78}{space 4} .7077112{col 91}{space 3} 1.443913
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.090424{col 50}{space 2} .3315547{col 61}{space 1}    0.28{col 70}{space 3}0.776{col 78}{space 4} .6007582{col 91}{space 3} 1.979207
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .7050556{col 50}{space 2} .2294216{col 61}{space 1}   -1.07{col 70}{space 3}0.283{col 78}{space 4} .3725306{col 91}{space 3} 1.334396
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}  .859264{col 50}{space 2} .2912932{col 61}{space 1}   -0.45{col 70}{space 3}0.655{col 78}{space 4} .4420572{col 91}{space 3} 1.670224
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.600942{col 50}{space 2} .2810704{col 61}{space 1}    2.68{col 70}{space 3}0.007{col 78}{space 4} 1.134718{col 91}{space 3} 2.258723
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.005969{col 50}{space 2} .3909627{col 61}{space 1}    0.02{col 70}{space 3}0.988{col 78}{space 4} .4695373{col 91}{space 3} 2.155257
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2}  1.09556{col 50}{space 2} .4040863{col 61}{space 1}    0.25{col 70}{space 3}0.805{col 78}{space 4} .5315977{col 91}{space 3}  2.25782
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .3170697{col 50}{space 2}  .116649{col 61}{space 1}   -3.12{col 70}{space 3}0.002{col 78}{space 4} .1541361{col 91}{space 3} .6522364
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.003973{col 50}{space 2} .2001391{col 61}{space 1}    0.02{col 70}{space 3}0.984{col 78}{space 4} .6791814{col 91}{space 3} 1.484084
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2}   .88363{col 50}{space 2} .1492986{col 61}{space 1}   -0.73{col 70}{space 3}0.464{col 78}{space 4}  .634464{col 91}{space 3} 1.230648
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .1480962{col 50}{space 2} .0590534{col 61}{space 1}   -4.79{col 70}{space 3}0.000{col 78}{space 4} .0677678{col 91}{space 3} .3236416
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 3: Worsen v Improve
. svy: logit AI_directionworse ib2.employmentstatus5 if infullmodel_Table2 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}   1{txt},{res}   1321{txt}){col 67}= {res}     27.86
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}  Linearized
{col 1}AI_directionworse{col 19}{c |} Odds Ratio{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
employmentstatus5 {c |}
{space 7}FT Worker  {c |}{col 19}{res}{space 2} .3361887{col 31}{space 2} .0694322{col 42}{space 1}   -5.28{col 51}{space 3}0.000{col 59}{space 4} .2241946{col 72}{space 3} .5041281
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 6.226455{col 31}{space 2} 1.203046{col 42}{space 1}    9.47{col 51}{space 3}0.000{col 59}{space 4} 4.262113{col 72}{space 3} 9.096132
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_directionworse ib2.employmentstatus5 $controls_demographics if infullmodel_Table2 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}  17{txt},{res}   1305{txt}){col 67}= {res}      4.64
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                   AI_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} .4188268{col 50}{space 2} .0960455{col 61}{space 1}   -3.80{col 70}{space 3}0.000{col 78}{space 4} .2670899{col 91}{space 3} .6567671
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.630793{col 50}{space 2} .3050056{col 61}{space 1}    2.61{col 70}{space 3}0.009{col 78}{space 4} 1.129935{col 91}{space 3} 2.353663
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.731111{col 50}{space 2} .3690646{col 61}{space 1}    2.57{col 70}{space 3}0.010{col 78}{space 4} 1.139427{col 91}{space 3} 2.630047
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} 1.343212{col 50}{space 2} .1961699{col 61}{space 1}    2.02{col 70}{space 3}0.044{col 78}{space 4} 1.008595{col 91}{space 3} 1.788845
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7325523{col 50}{space 2} .1120903{col 61}{space 1}   -2.03{col 70}{space 3}0.042{col 78}{space 4} .5425927{col 91}{space 3}  .989016
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.240947{col 50}{space 2} .2031517{col 61}{space 1}    1.32{col 70}{space 3}0.188{col 78}{space 4} .9000731{col 91}{space 3} 1.710916
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}  1.22502{col 50}{space 2} .4068413{col 61}{space 1}    0.61{col 70}{space 3}0.541{col 78}{space 4} .6385446{col 91}{space 3} 2.350146
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.053222{col 50}{space 2} .2908356{col 61}{space 1}    0.19{col 70}{space 3}0.851{col 78}{space 4} .6127091{col 91}{space 3} 1.810445
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .6364657{col 50}{space 2}  .396653{col 61}{space 1}   -0.72{col 70}{space 3}0.469{col 78}{space 4} .1874172{col 91}{space 3} 2.161426
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.921637{col 50}{space 2} .6822025{col 61}{space 1}    1.84{col 70}{space 3}0.066{col 78}{space 4} .9576527{col 91}{space 3} 3.855977
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 2.194025{col 50}{space 2} .7060782{col 61}{space 1}    2.44{col 70}{space 3}0.015{col 78}{space 4} 1.166965{col 91}{space 3} 4.125013
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.954344{col 50}{space 2} .5733307{col 61}{space 1}    2.28{col 70}{space 3}0.023{col 78}{space 4} 1.099161{col 91}{space 3} 3.474884
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.511639{col 50}{space 2} .4308267{col 61}{space 1}    1.45{col 70}{space 3}0.147{col 78}{space 4} .8642265{col 91}{space 3} 2.644042
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.146499{col 50}{space 2} .2142211{col 61}{space 1}    0.73{col 70}{space 3}0.464{col 78}{space 4} .7946625{col 91}{space 3} 1.654112
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 2.509639{col 50}{space 2} .6072188{col 61}{space 1}    3.80{col 70}{space 3}0.000{col 78}{space 4} 1.561242{col 91}{space 3} 4.034152
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.649087{col 50}{space 2} .5168764{col 61}{space 1}    1.60{col 70}{space 3}0.111{col 78}{space 4}  .891672{col 91}{space 3} 3.049874
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.619572{col 50}{space 2} .4988888{col 61}{space 1}    1.57{col 70}{space 3}0.118{col 78}{space 4} .8850298{col 91}{space 3} 2.963757
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 1.597944{col 50}{space 2} .5904314{col 61}{space 1}    1.27{col 70}{space 3}0.205{col 78}{space 4} .7740322{col 91}{space 3} 3.298863
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_directionworse ib2.employmentstatus5 $controls_demographics $controls_objective if infullmodel_Table2 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}  23{txt},{res}   1299{txt}){col 67}= {res}      4.25
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                   AI_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2}   .41444{col 50}{space 2} .0947479{col 61}{space 1}   -3.85{col 70}{space 3}0.000{col 78}{space 4} .2646575{col 91}{space 3} .6489916
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.643828{col 50}{space 2} .3076717{col 61}{space 1}    2.66{col 70}{space 3}0.008{col 78}{space 4} 1.138657{col 91}{space 3} 2.373122
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.718449{col 50}{space 2} .3686662{col 61}{space 1}    2.52{col 70}{space 3}0.012{col 78}{space 4} 1.128125{col 91}{space 3} 2.617677
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} 1.335884{col 50}{space 2} .1975336{col 61}{space 1}    1.96{col 70}{space 3}0.050{col 78}{space 4} .9995127{col 91}{space 3} 1.785456
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7356201{col 50}{space 2} .1132835{col 61}{space 1}   -1.99{col 70}{space 3}0.046{col 78}{space 4} .5438144{col 91}{space 3} .9950766
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.315665{col 50}{space 2} .2214155{col 61}{space 1}    1.63{col 70}{space 3}0.103{col 78}{space 4} .9457228{col 91}{space 3} 1.830319
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.318259{col 50}{space 2} .4386386{col 61}{space 1}    0.83{col 70}{space 3}0.406{col 78}{space 4}  .686296{col 91}{space 3} 2.532153
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2}  .926384{col 50}{space 2} .2584575{col 61}{space 1}   -0.27{col 70}{space 3}0.784{col 78}{space 4} .5359093{col 91}{space 3} 1.601367
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5966104{col 50}{space 2} .3877683{col 61}{space 1}   -0.79{col 70}{space 3}0.427{col 78}{space 4} .1667029{col 91}{space 3}   2.1352
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.823815{col 50}{space 2} .6373137{col 61}{space 1}    1.72{col 70}{space 3}0.086{col 78}{space 4} .9188914{col 91}{space 3} 3.619906
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 2.220856{col 50}{space 2} .7064391{col 61}{space 1}    2.51{col 70}{space 3}0.012{col 78}{space 4} 1.189901{col 91}{space 3} 4.145053
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.988575{col 50}{space 2} .5775212{col 61}{space 1}    2.37{col 70}{space 3}0.018{col 78}{space 4} 1.124889{col 91}{space 3} 3.515397
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.569265{col 50}{space 2} .4418033{col 61}{space 1}    1.60{col 70}{space 3}0.110{col 78}{space 4} .9033028{col 91}{space 3}  2.72621
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}  .926379{col 50}{space 2} .1941269{col 61}{space 1}   -0.36{col 70}{space 3}0.715{col 78}{space 4} .6141183{col 91}{space 3} 1.397415
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} .8738151{col 50}{space 2} .3061096{col 61}{space 1}   -0.39{col 70}{space 3}0.700{col 78}{space 4} .4394994{col 91}{space 3} 1.737324
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.107186{col 50}{space 2} .4118154{col 61}{space 1}    0.27{col 70}{space 3}0.784{col 78}{space 4} .5337372{col 91}{space 3} 2.296748
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .9733152{col 50}{space 2}   .39149{col 61}{space 1}   -0.07{col 70}{space 3}0.946{col 78}{space 4} .4421467{col 91}{space 3} 2.142597
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .7403207{col 50}{space 2} .1530683{col 61}{space 1}   -1.45{col 70}{space 3}0.146{col 78}{space 4} .4934744{col 91}{space 3} 1.110645
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.080125{col 50}{space 2} .4675268{col 61}{space 1}    0.18{col 70}{space 3}0.859{col 78}{space 4} .4620594{col 91}{space 3} 2.524936
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} .8527179{col 50}{space 2} .3655586{col 61}{space 1}   -0.37{col 70}{space 3}0.710{col 78}{space 4} .3677581{col 91}{space 3} 1.977191
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} 3.897365{col 50}{space 2}  1.55288{col 61}{space 1}    3.41{col 70}{space 3}0.001{col 78}{space 4} 1.783628{col 91}{space 3} 8.516043
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .9586039{col 50}{space 2} .2224665{col 61}{space 1}   -0.18{col 70}{space 3}0.855{col 78}{space 4} .6080186{col 91}{space 3} 1.511338
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.227994{col 50}{space 2} .2313644{col 61}{space 1}    1.09{col 70}{space 3}0.276{col 78}{space 4} .8485466{col 91}{space 3}  1.77712
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 2.090557{col 50}{space 2} .9456777{col 61}{space 1}    1.63{col 70}{space 3}0.103{col 78}{space 4} .8607202{col 91}{space 3}  5.07764
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. 
. 
. 
. ************************************************************************************************************
. 
. * FIGURE 2: Subjective Exposure to AI and Selected Political Preferences (Raw Data) *
.   
. * Set Control Variables  
. global controls_objective aiexposure_felten2021 i.aiexposure_pizzinelli2023 i.aiexposure_gmyrek2023  
{txt}
{com}. global controls_demographics age i.female_dummy i.degree_dummy i.employmentstatus5 i.employsector5 i.equivincomeHHquintile_HBAI_imp i.isco08_5group
{txt}
{com}. global controls_politics  i.pid2024 i.profile_leftrightplacement         
{txt}
{com}. 
. *Set Sample Size
. svy: reg polpreferences1_redistribution i.jobimpact2_AI $controls_demographics $controls_politics $controls_objective
{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,633
{txt}{col 1}Number of PSUs{col 20}= {res}    3,633{txt}{col 49}Population size{col 67}={res}  3,737.245
{txt}{col 49}Design df{col 67}= {res}     3,632
{txt}{col 49}F({res}  39{txt},{res}   3594{txt}){col 67}= {res}     36.62
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.2608

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}      polpreferences1_redistribution{col 38}{c |}      Coef.{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}jobimpact2_AI {c |}
{space 29}Worsen  {c |}{col 38}{res}{space 2} .1055692{col 50}{space 2} .0505023{col 61}{space 1}    2.09{col 70}{space 3}0.037{col 78}{space 4} .0065535{col 91}{space 3}  .204585
{txt}{space 28}Improve  {c |}{col 38}{res}{space 2} .0948219{col 50}{space 2} .0735572{col 61}{space 1}    1.29{col 70}{space 3}0.197{col 78}{space 4}-.0493956{col 91}{space 3} .2390393
{txt}{space 25}Don't Know  {c |}{col 38}{res}{space 2}  .013855{col 50}{space 2} .0590388{col 61}{space 1}    0.23{col 70}{space 3}0.814{col 78}{space 4}-.1018975{col 91}{space 3} .1296075
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2}  .001282{col 50}{space 2} .0018224{col 61}{space 1}    0.70{col 70}{space 3}0.482{col 78}{space 4}-.0022911{col 91}{space 3} .0048551
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}-.1103217{col 50}{space 2} .0442056{col 61}{space 1}   -2.50{col 70}{space 3}0.013{col 78}{space 4}-.1969921{col 91}{space 3}-.0236514
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .0462259{col 50}{space 2} .0457156{col 61}{space 1}    1.01{col 70}{space 3}0.312{col 78}{space 4}-.0434049{col 91}{space 3} .1358566
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} -.022575{col 50}{space 2} .0518905{col 61}{space 1}   -0.44{col 70}{space 3}0.664{col 78}{space 4}-.1243125{col 91}{space 3} .0791625
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .0258314{col 50}{space 2} .0479135{col 61}{space 1}    0.54{col 70}{space 3}0.590{col 78}{space 4}-.0681087{col 91}{space 3} .1197715
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .1531819{col 50}{space 2}  .089119{col 61}{space 1}    1.72{col 70}{space 3}0.086{col 78}{space 4}-.0215463{col 91}{space 3} .3279101
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2}-.1683704{col 50}{space 2} .0738443{col 61}{space 1}   -2.28{col 70}{space 3}0.023{col 78}{space 4}-.3131509{col 91}{space 3}-.0235899
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .2221254{col 50}{space 2} .1209937{col 61}{space 1}    1.84{col 70}{space 3}0.066{col 78}{space 4}-.0150969{col 91}{space 3} .4593477
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}-.0377159{col 50}{space 2} .0809142{col 61}{space 1}   -0.47{col 70}{space 3}0.641{col 78}{space 4}-.1963577{col 91}{space 3} .1209259
{txt}{space 34}3  {c |}{col 38}{res}{space 2} -.063061{col 50}{space 2} .0823252{col 61}{space 1}   -0.77{col 70}{space 3}0.444{col 78}{space 4}-.2244693{col 91}{space 3} .0983473
{txt}{space 34}4  {c |}{col 38}{res}{space 2}-.2810713{col 50}{space 2} .0815204{col 61}{space 1}   -3.45{col 70}{space 3}0.001{col 78}{space 4}-.4409015{col 91}{space 3} -.121241
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2}-.4132883{col 50}{space 2} .0827212{col 61}{space 1}   -5.00{col 70}{space 3}0.000{col 78}{space 4}-.5754729{col 91}{space 3}-.2511037
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}-.0155373{col 50}{space 2} .0627718{col 61}{space 1}   -0.25{col 70}{space 3}0.805{col 78}{space 4}-.1386088{col 91}{space 3} .1075342
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}-.2636036{col 50}{space 2} .1093962{col 61}{space 1}   -2.41{col 70}{space 3}0.016{col 78}{space 4}-.4780876{col 91}{space 3}-.0491196
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2}-.1081665{col 50}{space 2} .0776321{col 61}{space 1}   -1.39{col 70}{space 3}0.164{col 78}{space 4}-.2603732{col 91}{space 3} .0440403
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}-.0011309{col 50}{space 2} .0995273{col 61}{space 1}   -0.01{col 70}{space 3}0.991{col 78}{space 4}-.1962658{col 91}{space 3} .1940039
{txt}{space 36} {c |}
{space 29}pid2024 {c |}
{space 29}Labour  {c |}{col 38}{res}{space 2} .7377595{col 50}{space 2} .0817099{col 61}{space 1}    9.03{col 70}{space 3}0.000{col 78}{space 4} .5775577{col 91}{space 3} .8979613
{txt}{space 28}Liberal  {c |}{col 38}{res}{space 2} .7153253{col 50}{space 2} .1036983{col 61}{space 1}    6.90{col 70}{space 3}0.000{col 78}{space 4} .5120125{col 91}{space 3}  .918638
{txt}{space 30}Green  {c |}{col 38}{res}{space 2} .6940048{col 50}{space 2} .1058397{col 61}{space 1}    6.56{col 70}{space 3}0.000{col 78}{space 4} .4864936{col 91}{space 3}  .901516
{txt}{space 29}Reform  {c |}{col 38}{res}{space 2} .1602423{col 50}{space 2} .1324792{col 61}{space 1}    1.21{col 70}{space 3}0.227{col 78}{space 4}-.0994987{col 91}{space 3} .4199833
{txt}{space 30}Other  {c |}{col 38}{res}{space 2} .6586456{col 50}{space 2} .1186378{col 61}{space 1}    5.55{col 70}{space 3}0.000{col 78}{space 4} .4260422{col 91}{space 3}  .891249
{txt}{space 25}Don't Know  {c |}{col 38}{res}{space 2} .4363632{col 50}{space 2} .1049672{col 61}{space 1}    4.16{col 70}{space 3}0.000{col 78}{space 4} .2305627{col 91}{space 3} .6421637
{txt}{space 31}None  {c |}{col 38}{res}{space 2} .5491572{col 50}{space 2} .0777115{col 61}{space 1}    7.07{col 70}{space 3}0.000{col 78}{space 4} .3967946{col 91}{space 3} .7015197
{txt}{space 36} {c |}
{space 10}profile_leftrightplacement {c |}
{space 19}Fairly left-wing  {c |}{col 38}{res}{space 2}-.3134064{col 50}{space 2} .0850098{col 61}{space 1}   -3.69{col 70}{space 3}0.000{col 78}{space 4} -.480078{col 91}{space 3}-.1467347
{txt}{space 12}Slightly left-of-centre  {c |}{col 38}{res}{space 2}-.7476248{col 50}{space 2} .0893811{col 61}{space 1}   -8.36{col 70}{space 3}0.000{col 78}{space 4} -.922867{col 91}{space 3}-.5723826
{txt}{space 29}Centre  {c |}{col 38}{res}{space 2}-1.163259{col 50}{space 2} .0934701{col 61}{space 1}  -12.45{col 70}{space 3}0.000{col 78}{space 4}-1.346518{col 91}{space 3}-.9799998
{txt}{space 11}Slightly right-of-centre  {c |}{col 38}{res}{space 2}-1.616472{col 50}{space 2} .1092492{col 61}{space 1}  -14.80{col 70}{space 3}0.000{col 78}{space 4}-1.830668{col 91}{space 3}-1.402276
{txt}{space 18}Fairly right-wing  {c |}{col 38}{res}{space 2}-1.645728{col 50}{space 2} .1327517{col 61}{space 1}  -12.40{col 70}{space 3}0.000{col 78}{space 4}-1.906003{col 91}{space 3}-1.385453
{txt}{space 20}Very right-wing  {c |}{col 38}{res}{space 2}-1.742683{col 50}{space 2} .2496626{col 61}{space 1}   -6.98{col 70}{space 3}0.000{col 78}{space 4}-2.232176{col 91}{space 3} -1.25319
{txt}{space 25}Don’t know  {c |}{col 38}{res}{space 2}-1.197418{col 50}{space 2} .0908743{col 61}{space 1}  -13.18{col 70}{space 3}0.000{col 78}{space 4}-1.375587{col 91}{space 3}-1.019248
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2}-.0170566{col 50}{space 2} .0558349{col 61}{space 1}   -0.31{col 70}{space 3}0.760{col 78}{space 4}-.1265275{col 91}{space 3} .0924142
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2}-.1320424{col 50}{space 2} .1049473{col 61}{space 1}   -1.26{col 70}{space 3}0.208{col 78}{space 4}-.3378039{col 91}{space 3} .0737192
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2}-.2061814{col 50}{space 2} .1051801{col 61}{space 1}   -1.96{col 70}{space 3}0.050{col 78}{space 4}-.4123992{col 91}{space 3} .0000365
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .2111524{col 50}{space 2} .1232427{col 61}{space 1}    1.71{col 70}{space 3}0.087{col 78}{space 4}-.0304795{col 91}{space 3} .4527842
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .0027637{col 50}{space 2} .0658365{col 61}{space 1}    0.04{col 70}{space 3}0.967{col 78}{space 4}-.1263165{col 91}{space 3} .1318438
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} .1312173{col 50}{space 2} .0589496{col 61}{space 1}    2.23{col 70}{space 3}0.026{col 78}{space 4} .0156396{col 91}{space 3} .2467949
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 4.289795{col 50}{space 2} .1772194{col 61}{space 1}   24.21{col 70}{space 3}0.000{col 78}{space 4} 3.942336{col 91}{space 3} 4.637255
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. generate infullmodel_redist = e(sample)
{txt}
{com}. svy: reg polpreferences6_education i.jobimpact2_AI $controls_demographics $controls_politics $controls_objective
{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,537
{txt}{col 1}Number of PSUs{col 20}= {res}    3,537{txt}{col 49}Population size{col 67}={res} 3,649.7904
{txt}{col 49}Design df{col 67}= {res}     3,536
{txt}{col 49}F({res}  39{txt},{res}   3498{txt}){col 67}= {res}     21.80
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.2027

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}           polpreferences6_education{col 38}{c |}      Coef.{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}jobimpact2_AI {c |}
{space 29}Worsen  {c |}{col 38}{res}{space 2}-.0351956{col 50}{space 2} .0502827{col 61}{space 1}   -0.70{col 70}{space 3}0.484{col 78}{space 4}-.1337816{col 91}{space 3} .0633904
{txt}{space 28}Improve  {c |}{col 38}{res}{space 2} .3349367{col 50}{space 2} .0681122{col 61}{space 1}    4.92{col 70}{space 3}0.000{col 78}{space 4} .2013934{col 91}{space 3} .4684799
{txt}{space 25}Don't Know  {c |}{col 38}{res}{space 2}-.0059064{col 50}{space 2} .0569648{col 61}{space 1}   -0.10{col 70}{space 3}0.917{col 78}{space 4}-.1175936{col 91}{space 3} .1057808
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2} .0004198{col 50}{space 2} .0018274{col 61}{space 1}    0.23{col 70}{space 3}0.818{col 78}{space 4}-.0031632{col 91}{space 3} .0040028
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}-.0933917{col 50}{space 2} .0422919{col 61}{space 1}   -2.21{col 70}{space 3}0.027{col 78}{space 4}-.1763107{col 91}{space 3}-.0104727
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .1645408{col 50}{space 2} .0443555{col 61}{space 1}    3.71{col 70}{space 3}0.000{col 78}{space 4} .0775758{col 91}{space 3} .2515058
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .1584539{col 50}{space 2} .0515919{col 61}{space 1}    3.07{col 70}{space 3}0.002{col 78}{space 4} .0573011{col 91}{space 3} .2596068
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2}  .079805{col 50}{space 2} .0472987{col 61}{space 1}    1.69{col 70}{space 3}0.092{col 78}{space 4}-.0129304{col 91}{space 3} .1725405
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}-.0011325{col 50}{space 2} .0832354{col 61}{space 1}   -0.01{col 70}{space 3}0.989{col 78}{space 4}-.1643268{col 91}{space 3} .1620618
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .1524948{col 50}{space 2} .0670412{col 61}{space 1}    2.27{col 70}{space 3}0.023{col 78}{space 4} .0210515{col 91}{space 3} .2839381
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2}  .362627{col 50}{space 2} .1586247{col 61}{space 1}    2.29{col 70}{space 3}0.022{col 78}{space 4} .0516218{col 91}{space 3} .6736322
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .0740994{col 50}{space 2} .0809831{col 61}{space 1}    0.91{col 70}{space 3}0.360{col 78}{space 4}-.0846789{col 91}{space 3} .2328778
{txt}{space 34}3  {c |}{col 38}{res}{space 2}-.0011869{col 50}{space 2} .0794739{col 61}{space 1}   -0.01{col 70}{space 3}0.988{col 78}{space 4}-.1570061{col 91}{space 3} .1546324
{txt}{space 34}4  {c |}{col 38}{res}{space 2}-.1004544{col 50}{space 2} .0767597{col 61}{space 1}   -1.31{col 70}{space 3}0.191{col 78}{space 4} -.250952{col 91}{space 3} .0500433
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2}-.1059311{col 50}{space 2} .0799563{col 61}{space 1}   -1.32{col 70}{space 3}0.185{col 78}{space 4}-.2626961{col 91}{space 3}  .050834
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}-.0367639{col 50}{space 2} .0607841{col 61}{space 1}   -0.60{col 70}{space 3}0.545{col 78}{space 4}-.1559393{col 91}{space 3} .0824114
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} .0356075{col 50}{space 2} .1044856{col 61}{space 1}    0.34{col 70}{space 3}0.733{col 78}{space 4}-.1692507{col 91}{space 3} .2404658
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2}-.0088752{col 50}{space 2} .0806831{col 61}{space 1}   -0.11{col 70}{space 3}0.912{col 78}{space 4}-.1670652{col 91}{space 3} .1493149
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}-.1260558{col 50}{space 2} .0995952{col 61}{space 1}   -1.27{col 70}{space 3}0.206{col 78}{space 4}-.3213256{col 91}{space 3}  .069214
{txt}{space 36} {c |}
{space 29}pid2024 {c |}
{space 29}Labour  {c |}{col 38}{res}{space 2}  .262007{col 50}{space 2} .0759241{col 61}{space 1}    3.45{col 70}{space 3}0.001{col 78}{space 4} .1131476{col 91}{space 3} .4108664
{txt}{space 28}Liberal  {c |}{col 38}{res}{space 2} .3194845{col 50}{space 2} .1000014{col 61}{space 1}    3.19{col 70}{space 3}0.001{col 78}{space 4} .1234183{col 91}{space 3} .5155506
{txt}{space 30}Green  {c |}{col 38}{res}{space 2} .3723776{col 50}{space 2} .1078641{col 61}{space 1}    3.45{col 70}{space 3}0.001{col 78}{space 4} .1608954{col 91}{space 3} .5838598
{txt}{space 29}Reform  {c |}{col 38}{res}{space 2}-.2166618{col 50}{space 2} .1001926{col 61}{space 1}   -2.16{col 70}{space 3}0.031{col 78}{space 4}-.4131028{col 91}{space 3}-.0202207
{txt}{space 30}Other  {c |}{col 38}{res}{space 2} .4230259{col 50}{space 2} .1350682{col 61}{space 1}    3.13{col 70}{space 3}0.002{col 78}{space 4} .1582066{col 91}{space 3} .6878453
{txt}{space 25}Don't Know  {c |}{col 38}{res}{space 2} .2794445{col 50}{space 2} .0938785{col 61}{space 1}    2.98{col 70}{space 3}0.003{col 78}{space 4}  .095383{col 91}{space 3} .4635061
{txt}{space 31}None  {c |}{col 38}{res}{space 2} .0308751{col 50}{space 2} .0672775{col 61}{space 1}    0.46{col 70}{space 3}0.646{col 78}{space 4}-.1010316{col 91}{space 3} .1627818
{txt}{space 36} {c |}
{space 10}profile_leftrightplacement {c |}
{space 19}Fairly left-wing  {c |}{col 38}{res}{space 2}-.3307315{col 50}{space 2} .1189601{col 61}{space 1}   -2.78{col 70}{space 3}0.005{col 78}{space 4}-.5639688{col 91}{space 3}-.0974941
{txt}{space 12}Slightly left-of-centre  {c |}{col 38}{res}{space 2}-.7206547{col 50}{space 2} .1221966{col 61}{space 1}   -5.90{col 70}{space 3}0.000{col 78}{space 4}-.9602377{col 91}{space 3}-.4810716
{txt}{space 29}Centre  {c |}{col 38}{res}{space 2} -.989026{col 50}{space 2} .1233284{col 61}{space 1}   -8.02{col 70}{space 3}0.000{col 78}{space 4}-1.230828{col 91}{space 3}-.7472241
{txt}{space 11}Slightly right-of-centre  {c |}{col 38}{res}{space 2}-1.249679{col 50}{space 2} .1314978{col 61}{space 1}   -9.50{col 70}{space 3}0.000{col 78}{space 4}-1.507498{col 91}{space 3}-.9918598
{txt}{space 18}Fairly right-wing  {c |}{col 38}{res}{space 2}-1.405782{col 50}{space 2} .1423871{col 61}{space 1}   -9.87{col 70}{space 3}0.000{col 78}{space 4}-1.684951{col 91}{space 3}-1.126613
{txt}{space 20}Very right-wing  {c |}{col 38}{res}{space 2}-1.518217{col 50}{space 2} .2034459{col 61}{space 1}   -7.46{col 70}{space 3}0.000{col 78}{space 4}-1.917101{col 91}{space 3}-1.119334
{txt}{space 25}Don’t know  {c |}{col 38}{res}{space 2}-1.177068{col 50}{space 2} .1222226{col 61}{space 1}   -9.63{col 70}{space 3}0.000{col 78}{space 4}-1.416702{col 91}{space 3}-.9374343
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2}  .035949{col 50}{space 2}  .052046{col 61}{space 1}    0.69{col 70}{space 3}0.490{col 78}{space 4}-.0660943{col 91}{space 3} .1379922
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} .0699452{col 50}{space 2} .1028301{col 61}{space 1}    0.68{col 70}{space 3}0.496{col 78}{space 4}-.1316671{col 91}{space 3} .2715576
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} .0203655{col 50}{space 2} .1041149{col 61}{space 1}    0.20{col 70}{space 3}0.845{col 78}{space 4}-.1837658{col 91}{space 3} .2244968
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2}-.1996798{col 50}{space 2} .1187045{col 61}{space 1}   -1.68{col 70}{space 3}0.093{col 78}{space 4}-.4324159{col 91}{space 3} .0330564
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2}-.0918086{col 50}{space 2} .0642117{col 61}{space 1}   -1.43{col 70}{space 3}0.153{col 78}{space 4}-.2177043{col 91}{space 3} .0340871
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2}-.0070072{col 50}{space 2} .0589003{col 61}{space 1}   -0.12{col 70}{space 3}0.905{col 78}{space 4}-.1224891{col 91}{space 3} .1084747
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2}  3.34574{col 50}{space 2} .1882306{col 61}{space 1}   17.77{col 70}{space 3}0.000{col 78}{space 4} 2.976689{col 91}{space 3} 3.714792
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. generate infullmodel_education = e(sample)
{txt}
{com}. svy: reg polpreferences4_immigration    i.jobimpact2_AI $controls_demographics $controls_politics $controls_objective
{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,645
{txt}{col 1}Number of PSUs{col 20}= {res}    3,645{txt}{col 49}Population size{col 67}={res} 3,761.5796
{txt}{col 49}Design df{col 67}= {res}     3,644
{txt}{col 49}F({res}  39{txt},{res}   3606{txt}){col 67}= {res}     57.39
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.3297

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}         polpreferences4_immigration{col 38}{c |}      Coef.{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}jobimpact2_AI {c |}
{space 29}Worsen  {c |}{col 38}{res}{space 2} -.208027{col 50}{space 2} .0493213{col 61}{space 1}   -4.22{col 70}{space 3}0.000{col 78}{space 4}-.3047271{col 91}{space 3} -.111327
{txt}{space 28}Improve  {c |}{col 38}{res}{space 2} .1552427{col 50}{space 2} .0766498{col 61}{space 1}    2.03{col 70}{space 3}0.043{col 78}{space 4}  .004962{col 91}{space 3} .3055235
{txt}{space 25}Don't Know  {c |}{col 38}{res}{space 2}-.1085006{col 50}{space 2}  .056494{col 61}{space 1}   -1.92{col 70}{space 3}0.055{col 78}{space 4}-.2192635{col 91}{space 3} .0022623
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2}-.0119631{col 50}{space 2} .0017527{col 61}{space 1}   -6.83{col 70}{space 3}0.000{col 78}{space 4}-.0153994{col 91}{space 3}-.0085267
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .0422359{col 50}{space 2} .0432439{col 61}{space 1}    0.98{col 70}{space 3}0.329{col 78}{space 4}-.0425488{col 91}{space 3} .1270207
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .3189511{col 50}{space 2}  .045408{col 61}{space 1}    7.02{col 70}{space 3}0.000{col 78}{space 4} .2299235{col 91}{space 3} .4079787
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .1355655{col 50}{space 2}  .051204{col 61}{space 1}    2.65{col 70}{space 3}0.008{col 78}{space 4} .0351742{col 91}{space 3} .2359568
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .0370549{col 50}{space 2} .0464658{col 61}{space 1}    0.80{col 70}{space 3}0.425{col 78}{space 4}-.0540466{col 91}{space 3} .1281564
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .1634418{col 50}{space 2} .0849533{col 61}{space 1}    1.92{col 70}{space 3}0.054{col 78}{space 4}-.0031189{col 91}{space 3} .3300025
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .1145333{col 50}{space 2} .0778316{col 61}{space 1}    1.47{col 70}{space 3}0.141{col 78}{space 4}-.0380645{col 91}{space 3}  .267131
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .6025441{col 50}{space 2}  .152622{col 61}{space 1}    3.95{col 70}{space 3}0.000{col 78}{space 4} .3033112{col 91}{space 3} .9017771
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .1515783{col 50}{space 2} .0833631{col 61}{space 1}    1.82{col 70}{space 3}0.069{col 78}{space 4}-.0118646{col 91}{space 3} .3150212
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .1385061{col 50}{space 2} .0779738{col 61}{space 1}    1.78{col 70}{space 3}0.076{col 78}{space 4}-.0143706{col 91}{space 3} .2913828
{txt}{space 34}4  {c |}{col 38}{res}{space 2}  .181332{col 50}{space 2}  .075445{col 61}{space 1}    2.40{col 70}{space 3}0.016{col 78}{space 4} .0334133{col 91}{space 3} .3292506
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .3170084{col 50}{space 2} .0821159{col 61}{space 1}    3.86{col 70}{space 3}0.000{col 78}{space 4} .1560107{col 91}{space 3} .4780062
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}-.1170635{col 50}{space 2} .0617218{col 61}{space 1}   -1.90{col 70}{space 3}0.058{col 78}{space 4}-.2380762{col 91}{space 3} .0039492
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}-.1795786{col 50}{space 2} .0925368{col 61}{space 1}   -1.94{col 70}{space 3}0.052{col 78}{space 4}-.3610076{col 91}{space 3} .0018504
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2}-.1540218{col 50}{space 2} .0830966{col 61}{space 1}   -1.85{col 70}{space 3}0.064{col 78}{space 4}-.3169424{col 91}{space 3} .0088987
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}-.1671382{col 50}{space 2} .1089614{col 61}{space 1}   -1.53{col 70}{space 3}0.125{col 78}{space 4}-.3807696{col 91}{space 3} .0464932
{txt}{space 36} {c |}
{space 29}pid2024 {c |}
{space 29}Labour  {c |}{col 38}{res}{space 2} .3773833{col 50}{space 2} .0782941{col 61}{space 1}    4.82{col 70}{space 3}0.000{col 78}{space 4} .2238786{col 91}{space 3} .5308879
{txt}{space 28}Liberal  {c |}{col 38}{res}{space 2} .6032731{col 50}{space 2} .1061544{col 61}{space 1}    5.68{col 70}{space 3}0.000{col 78}{space 4} .3951451{col 91}{space 3} .8114011
{txt}{space 30}Green  {c |}{col 38}{res}{space 2}  .654981{col 50}{space 2} .1056069{col 61}{space 1}    6.20{col 70}{space 3}0.000{col 78}{space 4} .4479266{col 91}{space 3} .8620354
{txt}{space 29}Reform  {c |}{col 38}{res}{space 2} -.508061{col 50}{space 2} .0830595{col 61}{space 1}   -6.12{col 70}{space 3}0.000{col 78}{space 4}-.6709088{col 91}{space 3}-.3452133
{txt}{space 30}Other  {c |}{col 38}{res}{space 2} .5267196{col 50}{space 2} .1319776{col 61}{space 1}    3.99{col 70}{space 3}0.000{col 78}{space 4} .2679624{col 91}{space 3} .7854769
{txt}{space 25}Don't Know  {c |}{col 38}{res}{space 2} .3241368{col 50}{space 2} .0963942{col 61}{space 1}    3.36{col 70}{space 3}0.001{col 78}{space 4} .1351448{col 91}{space 3} .5131288
{txt}{space 31}None  {c |}{col 38}{res}{space 2} .1821247{col 50}{space 2} .0701614{col 61}{space 1}    2.60{col 70}{space 3}0.009{col 78}{space 4} .0445652{col 91}{space 3} .3196843
{txt}{space 36} {c |}
{space 10}profile_leftrightplacement {c |}
{space 19}Fairly left-wing  {c |}{col 38}{res}{space 2}-.4442855{col 50}{space 2} .1106493{col 61}{space 1}   -4.02{col 70}{space 3}0.000{col 78}{space 4}-.6612263{col 91}{space 3}-.2273447
{txt}{space 12}Slightly left-of-centre  {c |}{col 38}{res}{space 2}-.7824472{col 50}{space 2} .1143844{col 61}{space 1}   -6.84{col 70}{space 3}0.000{col 78}{space 4}-1.006711{col 91}{space 3}-.5581833
{txt}{space 29}Centre  {c |}{col 38}{res}{space 2}-1.118947{col 50}{space 2} .1182519{col 61}{space 1}   -9.46{col 70}{space 3}0.000{col 78}{space 4}-1.350793{col 91}{space 3}-.8871006
{txt}{space 11}Slightly right-of-centre  {c |}{col 38}{res}{space 2}-1.459984{col 50}{space 2} .1257269{col 61}{space 1}  -11.61{col 70}{space 3}0.000{col 78}{space 4}-1.706486{col 91}{space 3}-1.213482
{txt}{space 18}Fairly right-wing  {c |}{col 38}{res}{space 2}-1.678568{col 50}{space 2} .1376944{col 61}{space 1}  -12.19{col 70}{space 3}0.000{col 78}{space 4}-1.948534{col 91}{space 3}-1.408603
{txt}{space 20}Very right-wing  {c |}{col 38}{res}{space 2}-1.969072{col 50}{space 2} .1715861{col 61}{space 1}  -11.48{col 70}{space 3}0.000{col 78}{space 4}-2.305487{col 91}{space 3}-1.632658
{txt}{space 25}Don’t know  {c |}{col 38}{res}{space 2}-1.306213{col 50}{space 2}  .115668{col 61}{space 1}  -11.29{col 70}{space 3}0.000{col 78}{space 4}-1.532994{col 91}{space 3}-1.079433
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .0561296{col 50}{space 2}  .054422{col 61}{space 1}    1.03{col 70}{space 3}0.302{col 78}{space 4}-.0505709{col 91}{space 3} .1628302
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2}-.0734838{col 50}{space 2} .1105749{col 61}{space 1}   -0.66{col 70}{space 3}0.506{col 78}{space 4}-.2902787{col 91}{space 3} .1433111
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2}-.0792279{col 50}{space 2} .1084728{col 61}{space 1}   -0.73{col 70}{space 3}0.465{col 78}{space 4}-.2919013{col 91}{space 3} .1334455
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .0105041{col 50}{space 2} .1066825{col 61}{space 1}    0.10{col 70}{space 3}0.922{col 78}{space 4}-.1986592{col 91}{space 3} .2196673
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2}-.0194018{col 50}{space 2} .0632389{col 61}{space 1}   -0.31{col 70}{space 3}0.759{col 78}{space 4} -.143389{col 91}{space 3} .1045853
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} .1222299{col 50}{space 2} .0591923{col 61}{space 1}    2.06{col 70}{space 3}0.039{col 78}{space 4} .0061765{col 91}{space 3} .2382833
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 3.731615{col 50}{space 2} .1852556{col 61}{space 1}   20.14{col 70}{space 3}0.000{col 78}{space 4}   3.3684{col 91}{space 3} 4.094829
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. generate infullmodel_immig = e(sample)
{txt}
{com}. 
. *Standardise DVs
. egen std_redistribution = std(polpreferences1_redistribution) if infullmodel_redist == 1
{txt}(616 missing values generated)

{com}. egen std_education = std(polpreferences6_education)           if infullmodel_education == 1
{txt}(712 missing values generated)

{com}. egen std_immigration = std(polpreferences4_immigration)       if infullmodel_immig == 1
{txt}(604 missing values generated)

{com}. 
. 
. 
. *** Figure 2a: Support for Government Redistributing Incomes to the Less Well-Off ***
. 
. * M1: Bivariate Model
. svy: reg std_redistribution i.jobimpact2_AI if infullmodel_redist == 1
{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,633
{txt}{col 1}Number of PSUs{col 20}= {res}    3,633{txt}{col 49}Population size{col 67}={res}  3,737.245
{txt}{col 49}Design df{col 67}= {res}     3,632
{txt}{col 49}F({res}   3{txt},{res}   3630{txt}){col 67}= {res}      3.79
{txt}{col 49}Prob > F{col 67}= {res}    0.0100
{txt}{col 49}R-squared{col 67}= {res}    0.0037

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}  Linearized
{col 1}std_redistr~n{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
jobimpact2_AI {c |}
{space 6}Worsen  {c |}{col 15}{res}{space 2} .1354932{col 27}{space 2} .0441429{col 38}{space 1}    3.07{col 47}{space 3}0.002{col 55}{space 4} .0489458{col 68}{space 3} .2220405
{txt}{space 5}Improve  {c |}{col 15}{res}{space 2} .1121267{col 27}{space 2} .0610198{col 38}{space 1}    1.84{col 47}{space 3}0.066{col 55}{space 4}-.0075098{col 68}{space 3} .2317633
{txt}{space 2}Don't Know  {c |}{col 15}{res}{space 2} .0133839{col 27}{space 2} .0515771{col 38}{space 1}    0.26{col 47}{space 3}0.795{col 55}{space 4}-.0877391{col 68}{space 3}  .114507
{txt}{space 13} {c |}
{space 8}_cons {c |}{col 15}{res}{space 2}-.0548501{col 27}{space 2} .0259759{col 38}{space 1}   -2.11{col 47}{space 3}0.035{col 55}{space 4}-.1057789{col 68}{space 3}-.0039213
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. * M2: + Demographic Controls
. svy: reg std_redistribution i.jobimpact2_AI $controls_demographics if infullmodel_redist == 1
{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,633
{txt}{col 1}Number of PSUs{col 20}= {res}    3,633{txt}{col 49}Population size{col 67}={res}  3,737.245
{txt}{col 49}Design df{col 67}= {res}     3,632
{txt}{col 49}F({res}  19{txt},{res}   3614{txt}){col 67}= {res}      6.54
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.0385

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                  std_redistribution{col 38}{c |}      Coef.{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}jobimpact2_AI {c |}
{space 29}Worsen  {c |}{col 38}{res}{space 2} .1198606{col 50}{space 2} .0437025{col 61}{space 1}    2.74{col 70}{space 3}0.006{col 78}{space 4} .0341766{col 91}{space 3} .2055445
{txt}{space 28}Improve  {c |}{col 38}{res}{space 2} .0769991{col 50}{space 2} .0636263{col 61}{space 1}    1.21{col 70}{space 3}0.226{col 78}{space 4}-.0477477{col 91}{space 3} .2017459
{txt}{space 25}Don't Know  {c |}{col 38}{res}{space 2} .0006973{col 50}{space 2} .0511594{col 61}{space 1}    0.01{col 70}{space 3}0.989{col 78}{space 4}-.0996068{col 91}{space 3} .1010014
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2}-.0056873{col 50}{space 2} .0015643{col 61}{space 1}   -3.64{col 70}{space 3}0.000{col 78}{space 4}-.0087544{col 91}{space 3}-.0026203
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}-.0345369{col 50}{space 2}  .038053{col 61}{space 1}   -0.91{col 70}{space 3}0.364{col 78}{space 4}-.1091442{col 91}{space 3} .0400705
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .1907264{col 50}{space 2}  .039166{col 61}{space 1}    4.87{col 70}{space 3}0.000{col 78}{space 4} .1139368{col 91}{space 3} .2675159
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .0110249{col 50}{space 2} .0454987{col 61}{space 1}    0.24{col 70}{space 3}0.809{col 78}{space 4}-.0781806{col 91}{space 3} .1002304
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .0545898{col 50}{space 2} .0402423{col 61}{space 1}    1.36{col 70}{space 3}0.175{col 78}{space 4}-.0243099{col 91}{space 3} .1334895
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .2446633{col 50}{space 2} .0803838{col 61}{space 1}    3.04{col 70}{space 3}0.002{col 78}{space 4} .0870614{col 91}{space 3} .4022653
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2}-.0815248{col 50}{space 2} .0672345{col 61}{space 1}   -1.21{col 70}{space 3}0.225{col 78}{space 4}-.2133459{col 91}{space 3} .0502963
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .2343248{col 50}{space 2} .1029694{col 61}{space 1}    2.28{col 70}{space 3}0.023{col 78}{space 4} .0324411{col 91}{space 3} .4362085
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}-.0208994{col 50}{space 2} .0697563{col 61}{space 1}   -0.30{col 70}{space 3}0.764{col 78}{space 4}-.1576649{col 91}{space 3} .1158661
{txt}{space 34}3  {c |}{col 38}{res}{space 2}-.0916088{col 50}{space 2} .0706503{col 61}{space 1}   -1.30{col 70}{space 3}0.195{col 78}{space 4}-.2301271{col 91}{space 3} .0469095
{txt}{space 34}4  {c |}{col 38}{res}{space 2}-.2617838{col 50}{space 2}  .070223{col 61}{space 1}   -3.73{col 70}{space 3}0.000{col 78}{space 4}-.3994642{col 91}{space 3}-.1241035
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2}-.3858507{col 50}{space 2} .0718164{col 61}{space 1}   -5.37{col 70}{space 3}0.000{col 78}{space 4}-.5266552{col 91}{space 3}-.2450462
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}-.0021776{col 50}{space 2} .0522043{col 61}{space 1}   -0.04{col 70}{space 3}0.967{col 78}{space 4}-.1045303{col 91}{space 3}  .100175
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}-.0685628{col 50}{space 2} .0573998{col 61}{space 1}   -1.19{col 70}{space 3}0.232{col 78}{space 4}-.1811019{col 91}{space 3} .0439763
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2}-.0884524{col 50}{space 2} .0579917{col 61}{space 1}   -1.53{col 70}{space 3}0.127{col 78}{space 4}-.2021518{col 91}{space 3} .0252471
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .0636051{col 50}{space 2} .0639052{col 61}{space 1}    1.00{col 70}{space 3}0.320{col 78}{space 4}-.0616886{col 91}{space 3} .1888988
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2}  .322596{col 50}{space 2}  .109056{col 61}{space 1}    2.96{col 70}{space 3}0.003{col 78}{space 4} .1087788{col 91}{space 3} .5364131
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. * M3: + Political Controls
. svy: reg std_redistribution i.jobimpact2_AI $controls_demographics $controls_politics if infullmodel_redist == 1
{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,633
{txt}{col 1}Number of PSUs{col 20}= {res}    3,633{txt}{col 49}Population size{col 67}={res}  3,737.245
{txt}{col 49}Design df{col 67}= {res}     3,632
{txt}{col 49}F({res}  33{txt},{res}   3600{txt}){col 67}= {res}     42.20
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.2576

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                  std_redistribution{col 38}{c |}      Coef.{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}jobimpact2_AI {c |}
{space 29}Worsen  {c |}{col 38}{res}{space 2} .0837011{col 50}{space 2} .0389611{col 61}{space 1}    2.15{col 70}{space 3}0.032{col 78}{space 4} .0073132{col 91}{space 3} .1600889
{txt}{space 28}Improve  {c |}{col 38}{res}{space 2} .0651835{col 50}{space 2} .0565961{col 61}{space 1}    1.15{col 70}{space 3}0.250{col 78}{space 4}-.0457798{col 91}{space 3} .1761468
{txt}{space 25}Don't Know  {c |}{col 38}{res}{space 2} .0073944{col 50}{space 2} .0456163{col 61}{space 1}    0.16{col 70}{space 3}0.871{col 78}{space 4}-.0820417{col 91}{space 3} .0968305
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2} .0009912{col 50}{space 2} .0014074{col 61}{space 1}    0.70{col 70}{space 3}0.481{col 78}{space 4}-.0017681{col 91}{space 3} .0037506
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}-.0890812{col 50}{space 2} .0341113{col 61}{space 1}   -2.61{col 70}{space 3}0.009{col 78}{space 4}-.1559603{col 91}{space 3} -.022202
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .0313609{col 50}{space 2}  .035215{col 61}{space 1}    0.89{col 70}{space 3}0.373{col 78}{space 4}-.0376822{col 91}{space 3} .1004039
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} -.016291{col 50}{space 2} .0399492{col 61}{space 1}   -0.41{col 70}{space 3}0.683{col 78}{space 4} -.094616{col 91}{space 3}  .062034
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .0017855{col 50}{space 2} .0363551{col 61}{space 1}    0.05{col 70}{space 3}0.961{col 78}{space 4} -.069493{col 91}{space 3} .0730641
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .1012768{col 50}{space 2} .0683237{col 61}{space 1}    1.48{col 70}{space 3}0.138{col 78}{space 4}-.0326798{col 91}{space 3} .2352334
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2}-.1168835{col 50}{space 2} .0567131{col 61}{space 1}   -2.06{col 70}{space 3}0.039{col 78}{space 4}-.2280762{col 91}{space 3}-.0056909
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .1745796{col 50}{space 2} .0917948{col 61}{space 1}    1.90{col 70}{space 3}0.057{col 78}{space 4}-.0053948{col 91}{space 3} .3545541
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}-.0270396{col 50}{space 2} .0622641{col 61}{space 1}   -0.43{col 70}{space 3}0.664{col 78}{space 4}-.1491156{col 91}{space 3} .0950364
{txt}{space 34}3  {c |}{col 38}{res}{space 2}-.0535251{col 50}{space 2} .0635019{col 61}{space 1}   -0.84{col 70}{space 3}0.399{col 78}{space 4}-.1780281{col 91}{space 3} .0709779
{txt}{space 34}4  {c |}{col 38}{res}{space 2}-.2207606{col 50}{space 2} .0628921{col 61}{space 1}   -3.51{col 70}{space 3}0.000{col 78}{space 4}-.3440678{col 91}{space 3}-.0974533
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2}-.3247193{col 50}{space 2} .0635316{col 61}{space 1}   -5.11{col 70}{space 3}0.000{col 78}{space 4}-.4492805{col 91}{space 3}-.2001581
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .0490773{col 50}{space 2} .0459118{col 61}{space 1}    1.07{col 70}{space 3}0.285{col 78}{space 4}-.0409381{col 91}{space 3} .1390927
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}-.0637467{col 50}{space 2} .0503282{col 61}{space 1}   -1.27{col 70}{space 3}0.205{col 78}{space 4} -.162421{col 91}{space 3} .0349275
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} -.013148{col 50}{space 2} .0523073{col 61}{space 1}   -0.25{col 70}{space 3}0.802{col 78}{space 4}-.1157027{col 91}{space 3} .0894066
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .0970266{col 50}{space 2} .0587852{col 61}{space 1}    1.65{col 70}{space 3}0.099{col 78}{space 4}-.0182288{col 91}{space 3} .2122819
{txt}{space 36} {c |}
{space 29}pid2024 {c |}
{space 29}Labour  {c |}{col 38}{res}{space 2} .5703289{col 50}{space 2} .0630716{col 61}{space 1}    9.04{col 70}{space 3}0.000{col 78}{space 4} .4466697{col 91}{space 3} .6939882
{txt}{space 28}Liberal  {c |}{col 38}{res}{space 2} .5625712{col 50}{space 2} .0803256{col 61}{space 1}    7.00{col 70}{space 3}0.000{col 78}{space 4} .4050834{col 91}{space 3}  .720059
{txt}{space 30}Green  {c |}{col 38}{res}{space 2} .5378476{col 50}{space 2} .0824345{col 61}{space 1}    6.52{col 70}{space 3}0.000{col 78}{space 4} .3762252{col 91}{space 3}   .69947
{txt}{space 29}Reform  {c |}{col 38}{res}{space 2} .1263259{col 50}{space 2} .1020912{col 61}{space 1}    1.24{col 70}{space 3}0.216{col 78}{space 4}-.0738359{col 91}{space 3} .3264876
{txt}{space 30}Other  {c |}{col 38}{res}{space 2} .5119417{col 50}{space 2} .0909892{col 61}{space 1}    5.63{col 70}{space 3}0.000{col 78}{space 4} .3335467{col 91}{space 3} .6903368
{txt}{space 25}Don't Know  {c |}{col 38}{res}{space 2} .3357146{col 50}{space 2} .0808766{col 61}{space 1}    4.15{col 70}{space 3}0.000{col 78}{space 4} .1771466{col 91}{space 3} .4942827
{txt}{space 31}None  {c |}{col 38}{res}{space 2} .4238608{col 50}{space 2} .0603118{col 61}{space 1}    7.03{col 70}{space 3}0.000{col 78}{space 4} .3056124{col 91}{space 3} .5421091
{txt}{space 36} {c |}
{space 10}profile_leftrightplacement {c |}
{space 19}Fairly left-wing  {c |}{col 38}{res}{space 2}-.2455588{col 50}{space 2} .0653077{col 61}{space 1}   -3.76{col 70}{space 3}0.000{col 78}{space 4}-.3736022{col 91}{space 3}-.1175154
{txt}{space 12}Slightly left-of-centre  {c |}{col 38}{res}{space 2}-.5834137{col 50}{space 2} .0688718{col 61}{space 1}   -8.47{col 70}{space 3}0.000{col 78}{space 4}-.7184451{col 91}{space 3}-.4483824
{txt}{space 29}Centre  {c |}{col 38}{res}{space 2}-.9032752{col 50}{space 2} .0718004{col 61}{space 1}  -12.58{col 70}{space 3}0.000{col 78}{space 4}-1.044048{col 91}{space 3} -.762502
{txt}{space 11}Slightly right-of-centre  {c |}{col 38}{res}{space 2} -1.25019{col 50}{space 2} .0840696{col 61}{space 1}  -14.87{col 70}{space 3}0.000{col 78}{space 4}-1.415018{col 91}{space 3}-1.085362
{txt}{space 18}Fairly right-wing  {c |}{col 38}{res}{space 2} -1.27476{col 50}{space 2} .1024642{col 61}{space 1}  -12.44{col 70}{space 3}0.000{col 78}{space 4}-1.475653{col 91}{space 3}-1.073867
{txt}{space 20}Very right-wing  {c |}{col 38}{res}{space 2}-1.352962{col 50}{space 2} .1924707{col 61}{space 1}   -7.03{col 70}{space 3}0.000{col 78}{space 4}-1.730324{col 91}{space 3}-.9756009
{txt}{space 25}Don’t know  {c |}{col 38}{res}{space 2}-.9294918{col 50}{space 2} .0696424{col 61}{space 1}  -13.35{col 70}{space 3}0.000{col 78}{space 4}-1.066034{col 91}{space 3}-.7929498
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .5243362{col 50}{space 2} .1258544{col 61}{space 1}    4.17{col 70}{space 3}0.000{col 78}{space 4} .2775838{col 91}{space 3} .7710886
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. * M4: + Objective AI Exposure
. svy: reg std_redistribution i.jobimpact2_AI $controls_objective $controls_demographics $controls_politics if infullmodel_redist == 1                      
{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,633
{txt}{col 1}Number of PSUs{col 20}= {res}    3,633{txt}{col 49}Population size{col 67}={res}  3,737.245
{txt}{col 49}Design df{col 67}= {res}     3,632
{txt}{col 49}F({res}  39{txt},{res}   3594{txt}){col 67}= {res}     36.62
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.2608

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                  std_redistribution{col 38}{c |}      Coef.{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}jobimpact2_AI {c |}
{space 29}Worsen  {c |}{col 38}{res}{space 2} .0814185{col 50}{space 2} .0389491{col 61}{space 1}    2.09{col 70}{space 3}0.037{col 78}{space 4} .0050543{col 91}{space 3} .1577828
{txt}{space 28}Improve  {c |}{col 38}{res}{space 2} .0731298{col 50}{space 2} .0567297{col 61}{space 1}    1.29{col 70}{space 3}0.197{col 78}{space 4}-.0380955{col 91}{space 3} .1843551
{txt}{space 25}Don't Know  {c |}{col 38}{res}{space 2} .0106854{col 50}{space 2} .0455327{col 61}{space 1}    0.23{col 70}{space 3}0.814{col 78}{space 4}-.0785868{col 91}{space 3} .0999576
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2}-.0131547{col 50}{space 2} .0430617{col 61}{space 1}   -0.31{col 70}{space 3}0.760{col 78}{space 4}-.0975822{col 91}{space 3} .0712729
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2}-.1018355{col 50}{space 2} .0809389{col 61}{space 1}   -1.26{col 70}{space 3}0.208{col 78}{space 4}-.2605256{col 91}{space 3} .0568547
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2}-.1590139{col 50}{space 2} .0811184{col 61}{space 1}   -1.96{col 70}{space 3}0.050{col 78}{space 4} -.318056{col 91}{space 3} .0000281
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .1628478{col 50}{space 2} .0950489{col 61}{space 1}    1.71{col 70}{space 3}0.087{col 78}{space 4}-.0235068{col 91}{space 3} .3492023
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .0021314{col 50}{space 2} .0507753{col 61}{space 1}    0.04{col 70}{space 3}0.967{col 78}{space 4}-.0974195{col 91}{space 3} .1016824
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} .1011991{col 50}{space 2} .0454639{col 61}{space 1}    2.23{col 70}{space 3}0.026{col 78}{space 4} .0120618{col 91}{space 3} .1903365
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2} .0009887{col 50}{space 2} .0014055{col 61}{space 1}    0.70{col 70}{space 3}0.482{col 78}{space 4}-.0017669{col 91}{space 3} .0037444
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}-.0850838{col 50}{space 2} .0340929{col 61}{space 1}   -2.50{col 70}{space 3}0.013{col 78}{space 4}-.1519269{col 91}{space 3}-.0182407
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .0356509{col 50}{space 2} .0352574{col 61}{space 1}    1.01{col 70}{space 3}0.312{col 78}{space 4}-.0334753{col 91}{space 3} .1047771
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2}-.0174106{col 50}{space 2} .0400197{col 61}{space 1}   -0.44{col 70}{space 3}0.664{col 78}{space 4}-.0958739{col 91}{space 3} .0610527
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2}  .019922{col 50}{space 2} .0369525{col 61}{space 1}    0.54{col 70}{space 3}0.590{col 78}{space 4}-.0525277{col 91}{space 3} .0923717
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}  .118139{col 50}{space 2} .0687315{col 61}{space 1}    1.72{col 70}{space 3}0.086{col 78}{space 4}-.0166172{col 91}{space 3} .2528952
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2}-.1298529{col 50}{space 2} .0569512{col 61}{space 1}   -2.28{col 70}{space 3}0.023{col 78}{space 4}-.2415124{col 91}{space 3}-.0181933
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .1713105{col 50}{space 2} .0933144{col 61}{space 1}    1.84{col 70}{space 3}0.066{col 78}{space 4}-.0116432{col 91}{space 3} .3542643
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}-.0290878{col 50}{space 2} .0624037{col 61}{space 1}   -0.47{col 70}{space 3}0.641{col 78}{space 4}-.1514376{col 91}{space 3} .0932621
{txt}{space 34}3  {c |}{col 38}{res}{space 2}-.0486347{col 50}{space 2}  .063492{col 61}{space 1}   -0.77{col 70}{space 3}0.444{col 78}{space 4}-.1731182{col 91}{space 3} .0758487
{txt}{space 34}4  {c |}{col 38}{res}{space 2}-.2167716{col 50}{space 2} .0628712{col 61}{space 1}   -3.45{col 70}{space 3}0.001{col 78}{space 4} -.340038{col 91}{space 3}-.0935051
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2}-.3187417{col 50}{space 2} .0637973{col 61}{space 1}   -5.00{col 70}{space 3}0.000{col 78}{space 4}-.4438239{col 91}{space 3}-.1936596
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}-.0119829{col 50}{space 2} .0484117{col 61}{space 1}   -0.25{col 70}{space 3}0.805{col 78}{space 4}-.1068998{col 91}{space 3} .0829339
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}-.2032999{col 50}{space 2}   .08437{col 61}{space 1}   -2.41{col 70}{space 3}0.016{col 78}{space 4}-.3687171{col 91}{space 3}-.0378827
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2}-.0834216{col 50}{space 2} .0598724{col 61}{space 1}   -1.39{col 70}{space 3}0.164{col 78}{space 4}-.2008085{col 91}{space 3} .0339653
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}-.0008722{col 50}{space 2} .0767587{col 61}{space 1}   -0.01{col 70}{space 3}0.991{col 78}{space 4}-.1513667{col 91}{space 3} .1496223
{txt}{space 36} {c |}
{space 29}pid2024 {c |}
{space 29}Labour  {c |}{col 38}{res}{space 2} .5689848{col 50}{space 2} .0630174{col 61}{space 1}    9.03{col 70}{space 3}0.000{col 78}{space 4} .4454318{col 91}{space 3} .6925377
{txt}{space 28}Liberal  {c |}{col 38}{res}{space 2} .5516827{col 50}{space 2} .0799756{col 61}{space 1}    6.90{col 70}{space 3}0.000{col 78}{space 4} .3948812{col 91}{space 3} .7084843
{txt}{space 30}Green  {c |}{col 38}{res}{space 2} .5352397{col 50}{space 2} .0816271{col 61}{space 1}    6.56{col 70}{space 3}0.000{col 78}{space 4} .3752001{col 91}{space 3} .6952792
{txt}{space 29}Reform  {c |}{col 38}{res}{space 2} .1235842{col 50}{space 2} .1021724{col 61}{space 1}    1.21{col 70}{space 3}0.227{col 78}{space 4}-.0767367{col 91}{space 3} .3239051
{txt}{space 30}Other  {c |}{col 38}{res}{space 2} .5079694{col 50}{space 2} .0914975{col 61}{space 1}    5.55{col 70}{space 3}0.000{col 78}{space 4} .3285779{col 91}{space 3}  .687361
{txt}{space 25}Don't Know  {c |}{col 38}{res}{space 2} .3365379{col 50}{space 2} .0809542{col 61}{space 1}    4.16{col 70}{space 3}0.000{col 78}{space 4} .1778176{col 91}{space 3} .4952581
{txt}{space 31}None  {c |}{col 38}{res}{space 2} .4235283{col 50}{space 2} .0599337{col 61}{space 1}    7.07{col 70}{space 3}0.000{col 78}{space 4} .3060212{col 91}{space 3} .5410354
{txt}{space 36} {c |}
{space 10}profile_leftrightplacement {c |}
{space 19}Fairly left-wing  {c |}{col 38}{res}{space 2}-.2417095{col 50}{space 2} .0655624{col 61}{space 1}   -3.69{col 70}{space 3}0.000{col 78}{space 4}-.3702522{col 91}{space 3}-.1131667
{txt}{space 12}Slightly left-of-centre  {c |}{col 38}{res}{space 2}-.5765932{col 50}{space 2} .0689337{col 61}{space 1}   -8.36{col 70}{space 3}0.000{col 78}{space 4}-.7117458{col 91}{space 3}-.4414406
{txt}{space 29}Centre  {c |}{col 38}{res}{space 2}-.8971441{col 50}{space 2} .0720873{col 61}{space 1}  -12.45{col 70}{space 3}0.000{col 78}{space 4} -1.03848{col 91}{space 3}-.7558085
{txt}{space 11}Slightly right-of-centre  {c |}{col 38}{res}{space 2}-1.246677{col 50}{space 2} .0842566{col 61}{space 1}  -14.80{col 70}{space 3}0.000{col 78}{space 4}-1.411872{col 91}{space 3}-1.081482
{txt}{space 18}Fairly right-wing  {c |}{col 38}{res}{space 2} -1.26924{col 50}{space 2} .1023825{col 61}{space 1}  -12.40{col 70}{space 3}0.000{col 78}{space 4}-1.469973{col 91}{space 3}-1.068507
{txt}{space 20}Very right-wing  {c |}{col 38}{res}{space 2}-1.344015{col 50}{space 2} .1925481{col 61}{space 1}   -6.98{col 70}{space 3}0.000{col 78}{space 4}-1.721528{col 91}{space 3}-.9665021
{txt}{space 25}Don’t know  {c |}{col 38}{res}{space 2}-.9234884{col 50}{space 2} .0700853{col 61}{space 1}  -13.18{col 70}{space 3}0.000{col 78}{space 4}-1.060899{col 91}{space 3} -.786078
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .6100718{col 50}{space 2} .1366775{col 61}{space 1}    4.46{col 70}{space 3}0.000{col 78}{space 4} .3420994{col 91}{space 3} .8780441
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. *** Figure 2b: Support for Government Spending on Free Adult Education / Re-Training ***
. 
. * M1: Bivariate Model
. svy: reg std_education i.jobimpact2_AI if infullmodel_education == 1
{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,537
{txt}{col 1}Number of PSUs{col 20}= {res}    3,537{txt}{col 49}Population size{col 67}={res} 3,649.7904
{txt}{col 49}Design df{col 67}= {res}     3,536
{txt}{col 49}F({res}   3{txt},{res}   3534{txt}){col 67}= {res}     15.67
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.0146

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}  Linearized
{col 1}std_education{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
jobimpact2_AI {c |}
{space 6}Worsen  {c |}{col 15}{res}{space 2} .0604005{col 27}{space 2} .0461992{col 38}{space 1}    1.31{col 47}{space 3}0.191{col 55}{space 4}-.0301793{col 68}{space 3} .1509802
{txt}{space 5}Improve  {c |}{col 15}{res}{space 2} .4002883{col 27}{space 2}  .061118{col 38}{space 1}    6.55{col 47}{space 3}0.000{col 55}{space 4} .2804582{col 68}{space 3} .5201184
{txt}{space 2}Don't Know  {c |}{col 15}{res}{space 2}-.0273419{col 27}{space 2} .0508982{col 38}{space 1}   -0.54{col 47}{space 3}0.591{col 55}{space 4}-.1271347{col 68}{space 3} .0724509
{txt}{space 13} {c |}
{space 8}_cons {c |}{col 15}{res}{space 2}-.0889002{col 27}{space 2} .0261376{col 38}{space 1}   -3.40{col 47}{space 3}0.001{col 55}{space 4}-.1401465{col 68}{space 3}-.0376538
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. * M2: + Demographic Controls
. svy: reg std_education i.jobimpact2_AI $controls_demographics if infullmodel_education == 1
{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,537
{txt}{col 1}Number of PSUs{col 20}= {res}    3,537{txt}{col 49}Population size{col 67}={res} 3,649.7904
{txt}{col 49}Design df{col 67}= {res}     3,536
{txt}{col 49}F({res}  19{txt},{res}   3518{txt}){col 67}= {res}      9.87
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.0603

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                       std_education{col 38}{c |}      Coef.{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}jobimpact2_AI {c |}
{space 29}Worsen  {c |}{col 38}{res}{space 2} .0055005{col 50}{space 2}  .045675{col 61}{space 1}    0.12{col 70}{space 3}0.904{col 78}{space 4}-.0840514{col 91}{space 3} .0950524
{txt}{space 28}Improve  {c |}{col 38}{res}{space 2} .3110286{col 50}{space 2} .0628311{col 61}{space 1}    4.95{col 70}{space 3}0.000{col 78}{space 4} .1878398{col 91}{space 3} .4342174
{txt}{space 25}Don't Know  {c |}{col 38}{res}{space 2}-.0270963{col 50}{space 2} .0505017{col 61}{space 1}   -0.54{col 70}{space 3}0.592{col 78}{space 4}-.1261118{col 91}{space 3} .0719192
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2}-.0035892{col 50}{space 2} .0016153{col 61}{space 1}   -2.22{col 70}{space 3}0.026{col 78}{space 4}-.0067563{col 91}{space 3}-.0004221
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}-.0491851{col 50}{space 2} .0381771{col 61}{space 1}   -1.29{col 70}{space 3}0.198{col 78}{space 4}-.1240366{col 91}{space 3} .0256663
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .2873831{col 50}{space 2} .0397864{col 61}{space 1}    7.22{col 70}{space 3}0.000{col 78}{space 4} .2093765{col 91}{space 3} .3653897
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .1595557{col 50}{space 2} .0456721{col 61}{space 1}    3.49{col 70}{space 3}0.000{col 78}{space 4} .0700093{col 91}{space 3} .2491021
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .0941955{col 50}{space 2} .0411125{col 61}{space 1}    2.29{col 70}{space 3}0.022{col 78}{space 4}  .013589{col 91}{space 3} .1748021
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .1119919{col 50}{space 2} .0840479{col 61}{space 1}    1.33{col 70}{space 3}0.183{col 78}{space 4}-.0527953{col 91}{space 3}  .276779
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .1581352{col 50}{space 2} .0634799{col 61}{space 1}    2.49{col 70}{space 3}0.013{col 78}{space 4} .0336744{col 91}{space 3} .2825961
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .2949984{col 50}{space 2} .1334139{col 61}{space 1}    2.21{col 70}{space 3}0.027{col 78}{space 4} .0334225{col 91}{space 3} .5565743
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .0582548{col 50}{space 2}  .071501{col 61}{space 1}    0.81{col 70}{space 3}0.415{col 78}{space 4}-.0819325{col 91}{space 3} .1984422
{txt}{space 34}3  {c |}{col 38}{res}{space 2}-.0460113{col 50}{space 2} .0705286{col 61}{space 1}   -0.65{col 70}{space 3}0.514{col 78}{space 4}-.1842921{col 91}{space 3} .0922694
{txt}{space 34}4  {c |}{col 38}{res}{space 2}-.1281621{col 50}{space 2} .0679635{col 61}{space 1}   -1.89{col 70}{space 3}0.059{col 78}{space 4}-.2614138{col 91}{space 3} .0050895
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2}-.1273704{col 50}{space 2} .0703797{col 61}{space 1}   -1.81{col 70}{space 3}0.070{col 78}{space 4}-.2653593{col 91}{space 3} .0106185
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}-.0514574{col 50}{space 2} .0512407{col 61}{space 1}   -1.00{col 70}{space 3}0.315{col 78}{space 4}-.1519218{col 91}{space 3}  .049007
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}-.0696497{col 50}{space 2} .0572833{col 61}{space 1}   -1.22{col 70}{space 3}0.224{col 78}{space 4}-.1819613{col 91}{space 3} .0426618
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2}-.1225633{col 50}{space 2} .0587626{col 61}{space 1}   -2.09{col 70}{space 3}0.037{col 78}{space 4}-.2377753{col 91}{space 3}-.0073512
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}-.2592597{col 50}{space 2} .0649078{col 61}{space 1}   -3.99{col 70}{space 3}0.000{col 78}{space 4}-.3865202{col 91}{space 3}-.1319991
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .0337798{col 50}{space 2}  .109931{col 61}{space 1}    0.31{col 70}{space 3}0.759{col 78}{space 4}-.1817548{col 91}{space 3} .2493144
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. * M3: + Political Controls
. svy: reg std_education i.jobimpact2_AI $controls_demographics $controls_politics if infullmodel_education == 1
{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,537
{txt}{col 1}Number of PSUs{col 20}= {res}    3,537{txt}{col 49}Population size{col 67}={res} 3,649.7904
{txt}{col 49}Design df{col 67}= {res}     3,536
{txt}{col 49}F({res}  33{txt},{res}   3504{txt}){col 67}= {res}     25.01
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.2004

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                       std_education{col 38}{c |}      Coef.{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}jobimpact2_AI {c |}
{space 29}Worsen  {c |}{col 38}{res}{space 2}-.0239055{col 50}{space 2} .0422776{col 61}{space 1}   -0.57{col 70}{space 3}0.572{col 78}{space 4}-.1067965{col 91}{space 3} .0589854
{txt}{space 28}Improve  {c |}{col 38}{res}{space 2} .2901369{col 50}{space 2} .0572158{col 61}{space 1}    5.07{col 70}{space 3}0.000{col 78}{space 4} .1779576{col 91}{space 3} .4023161
{txt}{space 25}Don't Know  {c |}{col 38}{res}{space 2}-.0034255{col 50}{space 2} .0477724{col 61}{space 1}   -0.07{col 70}{space 3}0.943{col 78}{space 4}-.0970898{col 91}{space 3} .0902389
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2} .0002082{col 50}{space 2} .0015396{col 61}{space 1}    0.14{col 70}{space 3}0.892{col 78}{space 4}-.0028104{col 91}{space 3} .0032269
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}-.0835242{col 50}{space 2} .0355006{col 61}{space 1}   -2.35{col 70}{space 3}0.019{col 78}{space 4}-.1531279{col 91}{space 3}-.0139204
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .1367967{col 50}{space 2} .0373217{col 61}{space 1}    3.67{col 70}{space 3}0.000{col 78}{space 4} .0636226{col 91}{space 3} .2099709
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .1328634{col 50}{space 2} .0432104{col 61}{space 1}    3.07{col 70}{space 3}0.002{col 78}{space 4} .0481435{col 91}{space 3} .2175833
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2}  .064177{col 50}{space 2} .0387812{col 61}{space 1}    1.65{col 70}{space 3}0.098{col 78}{space 4}-.0118588{col 91}{space 3} .1402128
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}-.0054413{col 50}{space 2} .0695992{col 61}{space 1}   -0.08{col 70}{space 3}0.938{col 78}{space 4}-.1418999{col 91}{space 3} .1310173
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .1201528{col 50}{space 2} .0561295{col 61}{space 1}    2.14{col 70}{space 3}0.032{col 78}{space 4} .0101034{col 91}{space 3} .2302022
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .3041108{col 50}{space 2} .1340764{col 61}{space 1}    2.27{col 70}{space 3}0.023{col 78}{space 4} .0412359{col 91}{space 3} .5669858
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .0583834{col 50}{space 2} .0683112{col 61}{space 1}    0.85{col 70}{space 3}0.393{col 78}{space 4}  -.07555{col 91}{space 3} .1923167
{txt}{space 34}3  {c |}{col 38}{res}{space 2}-.0018638{col 50}{space 2}  .067319{col 61}{space 1}   -0.03{col 70}{space 3}0.978{col 78}{space 4}-.1338517{col 91}{space 3} .1301242
{txt}{space 34}4  {c |}{col 38}{res}{space 2}-.0842261{col 50}{space 2} .0650134{col 61}{space 1}   -1.30{col 70}{space 3}0.195{col 78}{space 4}-.2116937{col 91}{space 3} .0432415
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2}-.0843635{col 50}{space 2} .0677479{col 61}{space 1}   -1.25{col 70}{space 3}0.213{col 78}{space 4}-.2171925{col 91}{space 3} .0484655
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}-.0197542{col 50}{space 2} .0461904{col 61}{space 1}   -0.43{col 70}{space 3}0.669{col 78}{space 4}-.1103167{col 91}{space 3} .0708084
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}-.0544756{col 50}{space 2} .0525512{col 61}{space 1}   -1.04{col 70}{space 3}0.300{col 78}{space 4}-.1575094{col 91}{space 3} .0485582
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2}-.0474377{col 50}{space 2} .0555131{col 61}{space 1}   -0.85{col 70}{space 3}0.393{col 78}{space 4}-.1562786{col 91}{space 3} .0614032
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}-.1764096{col 50}{space 2} .0605499{col 61}{space 1}   -2.91{col 70}{space 3}0.004{col 78}{space 4}-.2951258{col 91}{space 3}-.0576934
{txt}{space 36} {c |}
{space 29}pid2024 {c |}
{space 29}Labour  {c |}{col 38}{res}{space 2} .2182132{col 50}{space 2} .0637645{col 61}{space 1}    3.42{col 70}{space 3}0.001{col 78}{space 4} .0931944{col 91}{space 3}  .343232
{txt}{space 28}Liberal  {c |}{col 38}{res}{space 2} .2659988{col 50}{space 2} .0844086{col 61}{space 1}    3.15{col 70}{space 3}0.002{col 78}{space 4} .1005043{col 91}{space 3} .4314933
{txt}{space 30}Green  {c |}{col 38}{res}{space 2} .3078093{col 50}{space 2}  .091219{col 61}{space 1}    3.37{col 70}{space 3}0.001{col 78}{space 4} .1289621{col 91}{space 3} .4866565
{txt}{space 29}Reform  {c |}{col 38}{res}{space 2} -.180303{col 50}{space 2} .0843716{col 61}{space 1}   -2.14{col 70}{space 3}0.033{col 78}{space 4} -.345725{col 91}{space 3} -.014881
{txt}{space 30}Other  {c |}{col 38}{res}{space 2} .3603181{col 50}{space 2} .1135424{col 61}{space 1}    3.17{col 70}{space 3}0.002{col 78}{space 4} .1377029{col 91}{space 3} .5829334
{txt}{space 25}Don't Know  {c |}{col 38}{res}{space 2} .2345916{col 50}{space 2} .0790158{col 61}{space 1}    2.97{col 70}{space 3}0.003{col 78}{space 4} .0796704{col 91}{space 3} .3895128
{txt}{space 31}None  {c |}{col 38}{res}{space 2} .0229964{col 50}{space 2} .0566295{col 61}{space 1}    0.41{col 70}{space 3}0.685{col 78}{space 4}-.0880334{col 91}{space 3} .1340262
{txt}{space 36} {c |}
{space 10}profile_leftrightplacement {c |}
{space 19}Fairly left-wing  {c |}{col 38}{res}{space 2}-.2876294{col 50}{space 2} .1015701{col 61}{space 1}   -2.83{col 70}{space 3}0.005{col 78}{space 4}-.4867713{col 91}{space 3}-.0884874
{txt}{space 12}Slightly left-of-centre  {c |}{col 38}{res}{space 2}-.6163661{col 50}{space 2} .1043543{col 61}{space 1}   -5.91{col 70}{space 3}0.000{col 78}{space 4}-.8209667{col 91}{space 3}-.4117654
{txt}{space 29}Centre  {c |}{col 38}{res}{space 2}-.8421414{col 50}{space 2} .1052636{col 61}{space 1}   -8.00{col 70}{space 3}0.000{col 78}{space 4}-1.048525{col 91}{space 3}-.6357579
{txt}{space 11}Slightly right-of-centre  {c |}{col 38}{res}{space 2}-1.066315{col 50}{space 2} .1119332{col 61}{space 1}   -9.53{col 70}{space 3}0.000{col 78}{space 4}-1.285775{col 91}{space 3}-.8468544
{txt}{space 18}Fairly right-wing  {c |}{col 38}{res}{space 2}-1.198685{col 50}{space 2} .1208895{col 61}{space 1}   -9.92{col 70}{space 3}0.000{col 78}{space 4}-1.435705{col 91}{space 3}-.9616647
{txt}{space 20}Very right-wing  {c |}{col 38}{res}{space 2}-1.292097{col 50}{space 2} .1711757{col 61}{space 1}   -7.55{col 70}{space 3}0.000{col 78}{space 4} -1.62771{col 91}{space 3}-.9564842
{txt}{space 25}Don’t know  {c |}{col 38}{res}{space 2}-1.002475{col 50}{space 2} .1041834{col 61}{space 1}   -9.62{col 70}{space 3}0.000{col 78}{space 4}-1.206741{col 91}{space 3}-.7982097
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .6069321{col 50}{space 2} .1501764{col 61}{space 1}    4.04{col 70}{space 3}0.000{col 78}{space 4} .3124909{col 91}{space 3} .9013733
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. * M4: + Objective AI Exposure
. svy: reg std_education i.jobimpact2_AI $controls_objective $controls_demographics $controls_politics if infullmodel_education == 1
{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,537
{txt}{col 1}Number of PSUs{col 20}= {res}    3,537{txt}{col 49}Population size{col 67}={res} 3,649.7904
{txt}{col 49}Design df{col 67}= {res}     3,536
{txt}{col 49}F({res}  39{txt},{res}   3498{txt}){col 67}= {res}     21.80
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.2027

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                       std_education{col 38}{c |}      Coef.{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}jobimpact2_AI {c |}
{space 29}Worsen  {c |}{col 38}{res}{space 2}-.0295992{col 50}{space 2} .0422873{col 61}{space 1}   -0.70{col 70}{space 3}0.484{col 78}{space 4}-.1125093{col 91}{space 3} .0533108
{txt}{space 28}Improve  {c |}{col 38}{res}{space 2} .2816792{col 50}{space 2} .0572819{col 61}{space 1}    4.92{col 70}{space 3}0.000{col 78}{space 4} .1693704{col 91}{space 3} .3939881
{txt}{space 25}Don't Know  {c |}{col 38}{res}{space 2}-.0049673{col 50}{space 2}  .047907{col 61}{space 1}   -0.10{col 70}{space 3}0.917{col 78}{space 4}-.0988954{col 91}{space 3} .0889608
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .0302328{col 50}{space 2} .0437703{col 61}{space 1}    0.69{col 70}{space 3}0.490{col 78}{space 4}-.0555848{col 91}{space 3} .1160504
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} .0588234{col 50}{space 2} .0864794{col 61}{space 1}    0.68{col 70}{space 3}0.496{col 78}{space 4}-.1107311{col 91}{space 3} .2283779
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} .0171272{col 50}{space 2} .0875598{col 61}{space 1}    0.20{col 70}{space 3}0.845{col 78}{space 4}-.1545456{col 91}{space 3} .1888001
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2}-.1679292{col 50}{space 2} .0998296{col 61}{space 1}   -1.68{col 70}{space 3}0.093{col 78}{space 4}-.3636586{col 91}{space 3} .0278002
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2}-.0772103{col 50}{space 2} .0540015{col 61}{space 1}   -1.43{col 70}{space 3}0.153{col 78}{space 4}-.1830877{col 91}{space 3}  .028667
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} -.005893{col 50}{space 2} .0495347{col 61}{space 1}   -0.12{col 70}{space 3}0.905{col 78}{space 4}-.1030124{col 91}{space 3} .0912264
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2}  .000353{col 50}{space 2} .0015369{col 61}{space 1}    0.23{col 70}{space 3}0.818{col 78}{space 4}-.0026602{col 91}{space 3} .0033663
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}-.0785417{col 50}{space 2} .0355672{col 61}{space 1}   -2.21{col 70}{space 3}0.027{col 78}{space 4} -.148276{col 91}{space 3}-.0088075
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .1383776{col 50}{space 2} .0373027{col 61}{space 1}    3.71{col 70}{space 3}0.000{col 78}{space 4} .0652407{col 91}{space 3} .2115145
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .1332586{col 50}{space 2} .0433884{col 61}{space 1}    3.07{col 70}{space 3}0.002{col 78}{space 4} .0481898{col 91}{space 3} .2183273
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .0671154{col 50}{space 2} .0397778{col 61}{space 1}    1.69{col 70}{space 3}0.092{col 78}{space 4}-.0108744{col 91}{space 3} .1451052
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}-.0009525{col 50}{space 2} .0700004{col 61}{space 1}   -0.01{col 70}{space 3}0.989{col 78}{space 4}-.1381976{col 91}{space 3} .1362927
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .1282469{col 50}{space 2} .0563811{col 61}{space 1}    2.27{col 70}{space 3}0.023{col 78}{space 4} .0177041{col 91}{space 3} .2387898
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .3049666{col 50}{space 2} .1334022{col 61}{space 1}    2.29{col 70}{space 3}0.022{col 78}{space 4} .0434136{col 91}{space 3} .5665197
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .0623171{col 50}{space 2} .0681062{col 61}{space 1}    0.91{col 70}{space 3}0.360{col 78}{space 4}-.0712143{col 91}{space 3} .1958485
{txt}{space 34}3  {c |}{col 38}{res}{space 2}-.0009981{col 50}{space 2} .0668369{col 61}{space 1}   -0.01{col 70}{space 3}0.988{col 78}{space 4} -.132041{col 91}{space 3} .1300447
{txt}{space 34}4  {c |}{col 38}{res}{space 2}-.0844814{col 50}{space 2} .0645543{col 61}{space 1}   -1.31{col 70}{space 3}0.191{col 78}{space 4}-.2110488{col 91}{space 3} .0420861
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2}-.0890872{col 50}{space 2} .0672426{col 61}{space 1}   -1.32{col 70}{space 3}0.185{col 78}{space 4}-.2209255{col 91}{space 3}  .042751
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}-.0309182{col 50}{space 2} .0511189{col 61}{space 1}   -0.60{col 70}{space 3}0.545{col 78}{space 4}-.1311437{col 91}{space 3} .0693074
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} .0299457{col 50}{space 2} .0878716{col 61}{space 1}    0.34{col 70}{space 3}0.733{col 78}{space 4}-.1423385{col 91}{space 3} .2022299
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2}-.0074639{col 50}{space 2} .0678538{col 61}{space 1}   -0.11{col 70}{space 3}0.912{col 78}{space 4}-.1405006{col 91}{space 3} .1255727
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} -.106012{col 50}{space 2} .0837588{col 61}{space 1}   -1.27{col 70}{space 3}0.206{col 78}{space 4}-.2702324{col 91}{space 3} .0582084
{txt}{space 36} {c |}
{space 29}pid2024 {c |}
{space 29}Labour  {c |}{col 38}{res}{space 2} .2203459{col 50}{space 2} .0638516{col 61}{space 1}    3.45{col 70}{space 3}0.001{col 78}{space 4} .0951563{col 91}{space 3} .3455355
{txt}{space 28}Liberal  {c |}{col 38}{res}{space 2} .2686841{col 50}{space 2} .0841004{col 61}{space 1}    3.19{col 70}{space 3}0.001{col 78}{space 4} .1037939{col 91}{space 3} .4335742
{txt}{space 30}Green  {c |}{col 38}{res}{space 2} .3131668{col 50}{space 2} .0907129{col 61}{space 1}    3.45{col 70}{space 3}0.001{col 78}{space 4} .1353118{col 91}{space 3} .4910217
{txt}{space 29}Reform  {c |}{col 38}{res}{space 2}-.1822109{col 50}{space 2} .0842612{col 61}{space 1}   -2.16{col 70}{space 3}0.031{col 78}{space 4}-.3474164{col 91}{space 3}-.0170055
{txt}{space 30}Other  {c |}{col 38}{res}{space 2} .3557616{col 50}{space 2} .1135913{col 61}{space 1}    3.13{col 70}{space 3}0.002{col 78}{space 4} .1330505{col 91}{space 3} .5784728
{txt}{space 25}Don't Know  {c |}{col 38}{res}{space 2} .2350108{col 50}{space 2} .0789511{col 61}{space 1}    2.98{col 70}{space 3}0.003{col 78}{space 4} .0802164{col 91}{space 3} .3898052
{txt}{space 31}None  {c |}{col 38}{res}{space 2} .0259658{col 50}{space 2} .0565799{col 61}{space 1}    0.46{col 70}{space 3}0.646{col 78}{space 4}-.0849668{col 91}{space 3} .1368983
{txt}{space 36} {c |}
{space 10}profile_leftrightplacement {c |}
{space 19}Fairly left-wing  {c |}{col 38}{res}{space 2}-.2781427{col 50}{space 2} .1000446{col 61}{space 1}   -2.78{col 70}{space 3}0.005{col 78}{space 4}-.4742936{col 91}{space 3}-.0819918
{txt}{space 12}Slightly left-of-centre  {c |}{col 38}{res}{space 2}-.6060652{col 50}{space 2} .1027665{col 61}{space 1}   -5.90{col 70}{space 3}0.000{col 78}{space 4}-.8075527{col 91}{space 3}-.4045776
{txt}{space 29}Centre  {c |}{col 38}{res}{space 2}-.8317635{col 50}{space 2} .1037183{col 61}{space 1}   -8.02{col 70}{space 3}0.000{col 78}{space 4}-1.035117{col 91}{space 3}-.6284098
{txt}{space 11}Slightly right-of-centre  {c |}{col 38}{res}{space 2}-1.050971{col 50}{space 2} .1105886{col 61}{space 1}   -9.50{col 70}{space 3}0.000{col 78}{space 4}-1.267795{col 91}{space 3}-.8341467
{txt}{space 18}Fairly right-wing  {c |}{col 38}{res}{space 2}-1.182252{col 50}{space 2} .1197465{col 61}{space 1}   -9.87{col 70}{space 3}0.000{col 78}{space 4}-1.417031{col 91}{space 3}-.9474727
{txt}{space 20}Very right-wing  {c |}{col 38}{res}{space 2}-1.276809{col 50}{space 2} .1710965{col 61}{space 1}   -7.46{col 70}{space 3}0.000{col 78}{space 4}-1.612267{col 91}{space 3}-.9413517
{txt}{space 25}Don’t know  {c |}{col 38}{res}{space 2}-.9899055{col 50}{space 2} .1027883{col 61}{space 1}   -9.63{col 70}{space 3}0.000{col 78}{space 4}-1.191436{col 91}{space 3}-.7883752
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .5447033{col 50}{space 2} .1583005{col 61}{space 1}    3.44{col 70}{space 3}0.001{col 78}{space 4} .2343337{col 91}{space 3} .8550729
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. *** Figure 2c: Support for Government Liberalising Britain's Immigration Controls ***
. 
. * M1: Bivariate Model
. svy: reg std_immigration i.jobimpact2_AI if infullmodel_immig == 1
{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,645
{txt}{col 1}Number of PSUs{col 20}= {res}    3,645{txt}{col 49}Population size{col 67}={res} 3,761.5796
{txt}{col 49}Design df{col 67}= {res}     3,644
{txt}{col 49}F({res}   3{txt},{res}   3642{txt}){col 67}= {res}     15.15
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.0147

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}  Linearized
{col 1}std_immigra~n{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
jobimpact2_AI {c |}
{space 6}Worsen  {c |}{col 15}{res}{space 2} -.033887{col 27}{space 2} .0459659{col 38}{space 1}   -0.74{col 47}{space 3}0.461{col 55}{space 4}-.1240084{col 68}{space 3} .0562345
{txt}{space 5}Improve  {c |}{col 15}{res}{space 2} .3601851{col 27}{space 2} .0628556{col 38}{space 1}    5.73{col 47}{space 3}0.000{col 55}{space 4} .2369493{col 68}{space 3} .4834208
{txt}{space 2}Don't Know  {c |}{col 15}{res}{space 2}-.1060287{col 27}{space 2} .0503158{col 38}{space 1}   -2.11{col 47}{space 3}0.035{col 55}{space 4}-.2046785{col 68}{space 3}-.0073789
{txt}{space 13} {c |}
{space 8}_cons {c |}{col 15}{res}{space 2}-.0586811{col 27}{space 2} .0251085{col 38}{space 1}   -2.34{col 47}{space 3}0.019{col 55}{space 4}-.1079092{col 68}{space 3} -.009453
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. * M2: + Demographic Controls
. svy: reg std_immigration i.jobimpact2_AI $controls_demographics if infullmodel_immig == 1
{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,645
{txt}{col 1}Number of PSUs{col 20}= {res}    3,645{txt}{col 49}Population size{col 67}={res} 3,761.5796
{txt}{col 49}Design df{col 67}= {res}     3,644
{txt}{col 49}F({res}  19{txt},{res}   3626{txt}){col 67}= {res}     31.13
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1444

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                     std_immigration{col 38}{c |}      Coef.{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}jobimpact2_AI {c |}
{space 29}Worsen  {c |}{col 38}{res}{space 2}-.1313939{col 50}{space 2} .0432045{col 61}{space 1}   -3.04{col 70}{space 3}0.002{col 78}{space 4}-.2161013{col 91}{space 3}-.0466864
{txt}{space 28}Improve  {c |}{col 38}{res}{space 2} .1427694{col 50}{space 2} .0610485{col 61}{space 1}    2.34{col 70}{space 3}0.019{col 78}{space 4} .0230767{col 91}{space 3} .2624621
{txt}{space 25}Don't Know  {c |}{col 38}{res}{space 2}-.0935048{col 50}{space 2} .0471992{col 61}{space 1}   -1.98{col 70}{space 3}0.048{col 78}{space 4}-.1860442{col 91}{space 3}-.0009654
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2}-.0141135{col 50}{space 2} .0014701{col 61}{space 1}   -9.60{col 70}{space 3}0.000{col 78}{space 4}-.0169959{col 91}{space 3}-.0112312
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .0827009{col 50}{space 2} .0359765{col 61}{space 1}    2.30{col 70}{space 3}0.022{col 78}{space 4} .0121649{col 91}{space 3}  .153237
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .4119814{col 50}{space 2} .0378373{col 61}{space 1}   10.89{col 70}{space 3}0.000{col 78}{space 4} .3377969{col 91}{space 3} .4861658
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2}  .128751{col 50}{space 2} .0431038{col 61}{space 1}    2.99{col 70}{space 3}0.003{col 78}{space 4}  .044241{col 91}{space 3} .2132609
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .0537609{col 50}{space 2} .0384542{col 61}{space 1}    1.40{col 70}{space 3}0.162{col 78}{space 4} -.021633{col 91}{space 3} .1291548
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .2337678{col 50}{space 2} .0807188{col 61}{space 1}    2.90{col 70}{space 3}0.004{col 78}{space 4} .0755093{col 91}{space 3} .3920263
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .1238569{col 50}{space 2} .0626908{col 61}{space 1}    1.98{col 70}{space 3}0.048{col 78}{space 4} .0009443{col 91}{space 3} .2467694
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2}  .472393{col 50}{space 2} .1186638{col 61}{space 1}    3.98{col 70}{space 3}0.000{col 78}{space 4}  .239739{col 91}{space 3} .7050471
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .1178451{col 50}{space 2} .0663208{col 61}{space 1}    1.78{col 70}{space 3}0.076{col 78}{space 4}-.0121844{col 91}{space 3} .2478746
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .0664381{col 50}{space 2} .0645571{col 61}{space 1}    1.03{col 70}{space 3}0.303{col 78}{space 4}-.0601336{col 91}{space 3} .1930098
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .0965981{col 50}{space 2} .0622363{col 61}{space 1}    1.55{col 70}{space 3}0.121{col 78}{space 4}-.0254234{col 91}{space 3} .2186197
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .2033184{col 50}{space 2} .0667863{col 61}{space 1}    3.04{col 70}{space 3}0.002{col 78}{space 4} .0723761{col 91}{space 3} .3342607
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}-.1039952{col 50}{space 2} .0502825{col 61}{space 1}   -2.07{col 70}{space 3}0.039{col 78}{space 4}-.2025798{col 91}{space 3}-.0054106
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}-.1664271{col 50}{space 2} .0528332{col 61}{space 1}   -3.15{col 70}{space 3}0.002{col 78}{space 4}-.2700127{col 91}{space 3}-.0628416
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2}-.2079497{col 50}{space 2} .0552596{col 61}{space 1}   -3.76{col 70}{space 3}0.000{col 78}{space 4}-.3162925{col 91}{space 3} -.099607
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}-.2651321{col 50}{space 2}   .06377{col 61}{space 1}   -4.16{col 70}{space 3}0.000{col 78}{space 4}-.3901605{col 91}{space 3}-.1401037
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2938119{col 50}{space 2} .0976851{col 61}{space 1}    3.01{col 70}{space 3}0.003{col 78}{space 4}  .102289{col 91}{space 3} .4853348
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. * M3: + Political Controls
. svy: reg std_immigration i.jobimpact2_AI $controls_demographics $controls_politics if infullmodel_immig == 1
{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,645
{txt}{col 1}Number of PSUs{col 20}= {res}    3,645{txt}{col 49}Population size{col 67}={res} 3,761.5796
{txt}{col 49}Design df{col 67}= {res}     3,644
{txt}{col 49}F({res}  33{txt},{res}   3612{txt}){col 67}= {res}     66.22
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.3275

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                     std_immigration{col 38}{c |}      Coef.{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}jobimpact2_AI {c |}
{space 29}Worsen  {c |}{col 38}{res}{space 2}-.1536399{col 50}{space 2} .0382298{col 61}{space 1}   -4.02{col 70}{space 3}0.000{col 78}{space 4}-.2285937{col 91}{space 3} -.078686
{txt}{space 28}Improve  {c |}{col 38}{res}{space 2} .1247672{col 50}{space 2} .0587732{col 61}{space 1}    2.12{col 70}{space 3}0.034{col 78}{space 4} .0095355{col 91}{space 3}  .239999
{txt}{space 25}Don't Know  {c |}{col 38}{res}{space 2} -.081469{col 50}{space 2} .0432958{col 61}{space 1}   -1.88{col 70}{space 3}0.060{col 78}{space 4}-.1663554{col 91}{space 3} .0034174
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2}-.0091712{col 50}{space 2}  .001346{col 61}{space 1}   -6.81{col 70}{space 3}0.000{col 78}{space 4}-.0118103{col 91}{space 3}-.0065322
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .0320373{col 50}{space 2} .0329392{col 61}{space 1}    0.97{col 70}{space 3}0.331{col 78}{space 4}-.0325438{col 91}{space 3} .0966183
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .2440605{col 50}{space 2} .0345538{col 61}{space 1}    7.06{col 70}{space 3}0.000{col 78}{space 4} .1763137{col 91}{space 3} .3118072
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .1032333{col 50}{space 2}  .039036{col 61}{space 1}    2.64{col 70}{space 3}0.008{col 78}{space 4} .0266988{col 91}{space 3} .1797678
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2}  .019536{col 50}{space 2} .0349254{col 61}{space 1}    0.56{col 70}{space 3}0.576{col 78}{space 4}-.0489393{col 91}{space 3} .0880113
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .1157726{col 50}{space 2} .0651394{col 61}{space 1}    1.78{col 70}{space 3}0.076{col 78}{space 4}-.0119407{col 91}{space 3}  .243486
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .0855364{col 50}{space 2} .0589274{col 61}{space 1}    1.45{col 70}{space 3}0.147{col 78}{space 4}-.0299975{col 91}{space 3} .2010702
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .4644275{col 50}{space 2} .1176688{col 61}{space 1}    3.95{col 70}{space 3}0.000{col 78}{space 4} .2337244{col 91}{space 3} .6951307
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .1163667{col 50}{space 2} .0636494{col 61}{space 1}    1.83{col 70}{space 3}0.068{col 78}{space 4}-.0084254{col 91}{space 3} .2411587
{txt}{space 34}3  {c |}{col 38}{res}{space 2}  .103963{col 50}{space 2} .0595608{col 61}{space 1}    1.75{col 70}{space 3}0.081{col 78}{space 4}-.0128128{col 91}{space 3} .2207387
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .1386382{col 50}{space 2} .0576259{col 61}{space 1}    2.41{col 70}{space 3}0.016{col 78}{space 4} .0256561{col 91}{space 3} .2516204
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .2470825{col 50}{space 2} .0629145{col 61}{space 1}    3.93{col 70}{space 3}0.000{col 78}{space 4} .1237314{col 91}{space 3} .3704336
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} -.061957{col 50}{space 2}  .044435{col 61}{space 1}   -1.39{col 70}{space 3}0.163{col 78}{space 4}-.1490769{col 91}{space 3} .0251629
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}-.1362353{col 50}{space 2} .0453347{col 61}{space 1}   -3.01{col 70}{space 3}0.003{col 78}{space 4}-.2251192{col 91}{space 3}-.0473515
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2}-.1429712{col 50}{space 2}  .052765{col 61}{space 1}   -2.71{col 70}{space 3}0.007{col 78}{space 4}-.2464232{col 91}{space 3}-.0395193
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}-.1879809{col 50}{space 2}  .058927{col 61}{space 1}   -3.19{col 70}{space 3}0.001{col 78}{space 4} -.303514{col 91}{space 3}-.0724477
{txt}{space 36} {c |}
{space 29}pid2024 {c |}
{space 29}Labour  {c |}{col 38}{res}{space 2} .2864209{col 50}{space 2} .0598638{col 61}{space 1}    4.78{col 70}{space 3}0.000{col 78}{space 4}  .169051{col 91}{space 3} .4037909
{txt}{space 28}Liberal  {c |}{col 38}{res}{space 2} .4649172{col 50}{space 2} .0808002{col 61}{space 1}    5.75{col 70}{space 3}0.000{col 78}{space 4} .3064992{col 91}{space 3} .6233353
{txt}{space 30}Green  {c |}{col 38}{res}{space 2} .4944647{col 50}{space 2} .0809966{col 61}{space 1}    6.10{col 70}{space 3}0.000{col 78}{space 4} .3356614{col 91}{space 3} .6532679
{txt}{space 29}Reform  {c |}{col 38}{res}{space 2}-.3876186{col 50}{space 2}  .063928{col 61}{space 1}   -6.06{col 70}{space 3}0.000{col 78}{space 4}-.5129568{col 91}{space 3}-.2622805
{txt}{space 30}Other  {c |}{col 38}{res}{space 2} .4047954{col 50}{space 2} .1011002{col 61}{space 1}    4.00{col 70}{space 3}0.000{col 78}{space 4} .2065768{col 91}{space 3} .6030139
{txt}{space 25}Don't Know  {c |}{col 38}{res}{space 2} .2450488{col 50}{space 2} .0738085{col 61}{space 1}    3.32{col 70}{space 3}0.001{col 78}{space 4} .1003387{col 91}{space 3}  .389759
{txt}{space 31}None  {c |}{col 38}{res}{space 2} .1350334{col 50}{space 2}  .053616{col 61}{space 1}    2.52{col 70}{space 3}0.012{col 78}{space 4}  .029913{col 91}{space 3} .2401537
{txt}{space 36} {c |}
{space 10}profile_leftrightplacement {c |}
{space 19}Fairly left-wing  {c |}{col 38}{res}{space 2}-.3473312{col 50}{space 2} .0850646{col 61}{space 1}   -4.08{col 70}{space 3}0.000{col 78}{space 4}-.5141102{col 91}{space 3}-.1805522
{txt}{space 12}Slightly left-of-centre  {c |}{col 38}{res}{space 2}-.6066361{col 50}{space 2} .0877872{col 61}{space 1}   -6.91{col 70}{space 3}0.000{col 78}{space 4} -.778753{col 91}{space 3}-.4345193
{txt}{space 29}Centre  {c |}{col 38}{res}{space 2}-.8657536{col 50}{space 2} .0907295{col 61}{space 1}   -9.54{col 70}{space 3}0.000{col 78}{space 4}-1.043639{col 91}{space 3} -.687868
{txt}{space 11}Slightly right-of-centre  {c |}{col 38}{res}{space 2}-1.129227{col 50}{space 2} .0964374{col 61}{space 1}  -11.71{col 70}{space 3}0.000{col 78}{space 4}-1.318304{col 91}{space 3}-.9401505
{txt}{space 18}Fairly right-wing  {c |}{col 38}{res}{space 2}-1.294189{col 50}{space 2}  .105695{col 61}{space 1}  -12.24{col 70}{space 3}0.000{col 78}{space 4}-1.501416{col 91}{space 3}-1.086962
{txt}{space 20}Very right-wing  {c |}{col 38}{res}{space 2}-1.515532{col 50}{space 2} .1313622{col 61}{space 1}  -11.54{col 70}{space 3}0.000{col 78}{space 4}-1.773082{col 91}{space 3}-1.257981
{txt}{space 25}Don’t know  {c |}{col 38}{res}{space 2}-1.007984{col 50}{space 2} .0888693{col 61}{space 1}  -11.34{col 70}{space 3}0.000{col 78}{space 4}-1.182222{col 91}{space 3}-.8337453
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .7950616{col 50}{space 2} .1301505{col 61}{space 1}    6.11{col 70}{space 3}0.000{col 78}{space 4} .5398867{col 91}{space 3} 1.050237
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. * M4: + Objective AI Exposure
. svy: reg std_immigration i.jobimpact2_AI $controls_objective $controls_demographics $controls_politics if infullmodel_immig == 1
{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,645
{txt}{col 1}Number of PSUs{col 20}= {res}    3,645{txt}{col 49}Population size{col 67}={res} 3,761.5796
{txt}{col 49}Design df{col 67}= {res}     3,644
{txt}{col 49}F({res}  39{txt},{res}   3606{txt}){col 67}= {res}     57.39
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.3297

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                     std_immigration{col 38}{c |}      Coef.{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}jobimpact2_AI {c |}
{space 29}Worsen  {c |}{col 38}{res}{space 2}-.1592315{col 50}{space 2} .0377523{col 61}{space 1}   -4.22{col 70}{space 3}0.000{col 78}{space 4}-.2332492{col 91}{space 3}-.0852137
{txt}{space 28}Improve  {c |}{col 38}{res}{space 2} .1188284{col 50}{space 2} .0586705{col 61}{space 1}    2.03{col 70}{space 3}0.043{col 78}{space 4} .0037981{col 91}{space 3} .2338588
{txt}{space 25}Don't Know  {c |}{col 38}{res}{space 2}-.0830503{col 50}{space 2} .0432425{col 61}{space 1}   -1.92{col 70}{space 3}0.055{col 78}{space 4}-.1678323{col 91}{space 3} .0017317
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .0429637{col 50}{space 2} .0416566{col 61}{space 1}    1.03{col 70}{space 3}0.302{col 78}{space 4}-.0387088{col 91}{space 3} .1246362
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2}-.0562471{col 50}{space 2} .0846381{col 61}{space 1}   -0.66{col 70}{space 3}0.506{col 78}{space 4}-.2221898{col 91}{space 3} .1096955
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2}-.0606439{col 50}{space 2}  .083029{col 61}{space 1}   -0.73{col 70}{space 3}0.465{col 78}{space 4}-.2234319{col 91}{space 3} .1021441
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .0080402{col 50}{space 2} .0816586{col 61}{space 1}    0.10{col 70}{space 3}0.922{col 78}{space 4}-.1520609{col 91}{space 3} .1681414
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2}-.0148509{col 50}{space 2} .0484054{col 61}{space 1}   -0.31{col 70}{space 3}0.759{col 78}{space 4}-.1097551{col 91}{space 3} .0800534
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} .0935592{col 50}{space 2}  .045308{col 61}{space 1}    2.06{col 70}{space 3}0.039{col 78}{space 4} .0047277{col 91}{space 3} .1823907
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2} -.009157{col 50}{space 2} .0013416{col 61}{space 1}   -6.83{col 70}{space 3}0.000{col 78}{space 4}-.0117873{col 91}{space 3}-.0065267
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .0323289{col 50}{space 2} .0331005{col 61}{space 1}    0.98{col 70}{space 3}0.329{col 78}{space 4}-.0325684{col 91}{space 3} .0972262
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .2441367{col 50}{space 2} .0347569{col 61}{space 1}    7.02{col 70}{space 3}0.000{col 78}{space 4} .1759918{col 91}{space 3} .3122817
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .1037667{col 50}{space 2} .0391934{col 61}{space 1}    2.65{col 70}{space 3}0.008{col 78}{space 4} .0269236{col 91}{space 3} .1806099
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .0283632{col 50}{space 2} .0355666{col 61}{space 1}    0.80{col 70}{space 3}0.425{col 78}{space 4}-.0413692{col 91}{space 3} .0980956
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .1251043{col 50}{space 2} .0650263{col 61}{space 1}    1.92{col 70}{space 3}0.054{col 78}{space 4}-.0023873{col 91}{space 3} .2525959
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .0876679{col 50}{space 2} .0595751{col 61}{space 1}    1.47{col 70}{space 3}0.141{col 78}{space 4} -.029136{col 91}{space 3} .2044718
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .4612092{col 50}{space 2} .1168224{col 61}{space 1}    3.95{col 70}{space 3}0.000{col 78}{space 4} .2321654{col 91}{space 3} .6902529
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .1160236{col 50}{space 2} .0638091{col 61}{space 1}    1.82{col 70}{space 3}0.069{col 78}{space 4}-.0090816{col 91}{space 3} .2411287
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .1060176{col 50}{space 2}  .059684{col 61}{space 1}    1.78{col 70}{space 3}0.076{col 78}{space 4}-.0109997{col 91}{space 3}  .223035
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .1387981{col 50}{space 2} .0577484{col 61}{space 1}    2.40{col 70}{space 3}0.016{col 78}{space 4} .0255758{col 91}{space 3} .2520204
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .2426498{col 50}{space 2} .0628545{col 61}{space 1}    3.86{col 70}{space 3}0.000{col 78}{space 4} .1194163{col 91}{space 3} .3658833
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}-.0896047{col 50}{space 2} .0472441{col 61}{space 1}   -1.90{col 70}{space 3}0.058{col 78}{space 4}-.1822322{col 91}{space 3} .0030229
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} -.137456{col 50}{space 2}  .070831{col 61}{space 1}   -1.94{col 70}{space 3}0.052{col 78}{space 4}-.2763283{col 91}{space 3} .0014164
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2}-.1178939{col 50}{space 2} .0636052{col 61}{space 1}   -1.85{col 70}{space 3}0.064{col 78}{space 4}-.2425992{col 91}{space 3} .0068114
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}-.1279336{col 50}{space 2}  .083403{col 61}{space 1}   -1.53{col 70}{space 3}0.125{col 78}{space 4}-.2914549{col 91}{space 3} .0355876
{txt}{space 36} {c |}
{space 29}pid2024 {c |}
{space 29}Labour  {c |}{col 38}{res}{space 2} .2888629{col 50}{space 2} .0599292{col 61}{space 1}    4.82{col 70}{space 3}0.000{col 78}{space 4} .1713648{col 91}{space 3} .4063609
{txt}{space 28}Liberal  {c |}{col 38}{res}{space 2} .4617672{col 50}{space 2} .0812545{col 61}{space 1}    5.68{col 70}{space 3}0.000{col 78}{space 4} .3024584{col 91}{space 3} .6210759
{txt}{space 30}Green  {c |}{col 38}{res}{space 2} .5013463{col 50}{space 2} .0808353{col 61}{space 1}    6.20{col 70}{space 3}0.000{col 78}{space 4} .3428593{col 91}{space 3} .6598332
{txt}{space 29}Reform  {c |}{col 38}{res}{space 2}-.3888884{col 50}{space 2} .0635768{col 61}{space 1}   -6.12{col 70}{space 3}0.000{col 78}{space 4} -.513538{col 91}{space 3}-.2642388
{txt}{space 30}Other  {c |}{col 38}{res}{space 2} .4031704{col 50}{space 2} .1010204{col 61}{space 1}    3.99{col 70}{space 3}0.000{col 78}{space 4} .2051082{col 91}{space 3} .6012326
{txt}{space 25}Don't Know  {c |}{col 38}{res}{space 2} .2481061{col 50}{space 2} .0737837{col 61}{space 1}    3.36{col 70}{space 3}0.001{col 78}{space 4} .1034447{col 91}{space 3} .3927675
{txt}{space 31}None  {c |}{col 38}{res}{space 2} .1394049{col 50}{space 2} .0537041{col 61}{space 1}    2.60{col 70}{space 3}0.009{col 78}{space 4} .0341118{col 91}{space 3}  .244698
{txt}{space 36} {c |}
{space 10}profile_leftrightplacement {c |}
{space 19}Fairly left-wing  {c |}{col 38}{res}{space 2}-.3400723{col 50}{space 2}  .084695{col 61}{space 1}   -4.02{col 70}{space 3}0.000{col 78}{space 4}-.5061266{col 91}{space 3}-.1740179
{txt}{space 12}Slightly left-of-centre  {c |}{col 38}{res}{space 2}-.5989135{col 50}{space 2}  .087554{col 61}{space 1}   -6.84{col 70}{space 3}0.000{col 78}{space 4}-.7705732{col 91}{space 3}-.4272538
{txt}{space 29}Centre  {c |}{col 38}{res}{space 2}-.8564827{col 50}{space 2} .0905143{col 61}{space 1}   -9.46{col 70}{space 3}0.000{col 78}{space 4}-1.033946{col 91}{space 3} -.679019
{txt}{space 11}Slightly right-of-centre  {c |}{col 38}{res}{space 2}-1.117525{col 50}{space 2} .0962359{col 61}{space 1}  -11.61{col 70}{space 3}0.000{col 78}{space 4}-1.306206{col 91}{space 3} -.928843
{txt}{space 18}Fairly right-wing  {c |}{col 38}{res}{space 2}-1.284837{col 50}{space 2} .1053963{col 61}{space 1}  -12.19{col 70}{space 3}0.000{col 78}{space 4}-1.491479{col 91}{space 3}-1.078196
{txt}{space 20}Very right-wing  {c |}{col 38}{res}{space 2}-1.507199{col 50}{space 2} .1313382{col 61}{space 1}  -11.48{col 70}{space 3}0.000{col 78}{space 4}-1.764703{col 91}{space 3}-1.249696
{txt}{space 25}Don’t know  {c |}{col 38}{res}{space 2}-.9998231{col 50}{space 2} .0885365{col 61}{space 1}  -11.29{col 70}{space 3}0.000{col 78}{space 4}-1.173409{col 91}{space 3}-.8262372
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .7681107{col 50}{space 2} .1418013{col 61}{space 1}    5.42{col 70}{space 3}0.000{col 78}{space 4} .4900929{col 91}{space 3} 1.046129
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. 
. 
. ******************************************* SUPPLEMENTARY MATERIAL *****************************************
. 
. ************************************************************************************************************
. 
. * APPENDIX A: Distribution of Subjective Exposure to AI by Objective Occupational Exposure - Alternative Exposure Indices (Raw Data) *
.         
. * Set Controls and Sample Size  
. global controls_occupindices std_RTI_Autor std_offshorable_Blinder2007 
{txt}
{com}. global controls_demographics age ib1.female_dummy i.degree_dummy ib2.employmentstatus5 i.employsector5 i.equivincomeHHquintile_HBAI_imp         
{txt}
{com}.  
. svy: logit AI_worsen aiexposure_schendstok2024 $controls_demographics  $controls_occupindices, or
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}  15{txt},{res}   3947{txt}){col 67}= {res}      7.21
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}aiexposure_schendstok2024 {c |}{col 38}{res}{space 2} 1.254052{col 50}{space 2} .0565886{col 61}{space 1}    5.02{col 70}{space 3}0.000{col 78}{space 4} 1.147872{col 91}{space 3} 1.370053
{txt}{space 33}age {c |}{col 38}{res}{space 2} .9931302{col 50}{space 2} .0036252{col 61}{space 1}   -1.89{col 70}{space 3}0.059{col 78}{space 4} .9860482{col 91}{space 3} 1.000263
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.105329{col 50}{space 2}  .096108{col 61}{space 1}    1.15{col 70}{space 3}0.249{col 78}{space 4} .9320891{col 91}{space 3} 1.310768
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.334116{col 50}{space 2} .1193482{col 61}{space 1}    3.22{col 70}{space 3}0.001{col 78}{space 4} 1.119497{col 91}{space 3} 1.589879
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} .7745197{col 50}{space 2} .0814953{col 61}{space 1}   -2.43{col 70}{space 3}0.015{col 78}{space 4} .6301462{col 91}{space 3} .9519707
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.029998{col 50}{space 2} .1004884{col 61}{space 1}    0.30{col 70}{space 3}0.762{col 78}{space 4} .8506798{col 91}{space 3} 1.247115
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9674227{col 50}{space 2} .1731842{col 61}{space 1}   -0.19{col 70}{space 3}0.853{col 78}{space 4} .6810688{col 91}{space 3} 1.374174
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.049129{col 50}{space 2} .1510132{col 61}{space 1}    0.33{col 70}{space 3}0.739{col 78}{space 4} .7911667{col 91}{space 3}   1.3912
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8510905{col 50}{space 2} .2437038{col 61}{space 1}   -0.56{col 70}{space 3}0.573{col 78}{space 4} .4854736{col 91}{space 3} 1.492059
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.102863{col 50}{space 2} .1839508{col 61}{space 1}    0.59{col 70}{space 3}0.557{col 78}{space 4} .7952484{col 91}{space 3} 1.529467
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.022112{col 50}{space 2} .1657357{col 61}{space 1}    0.13{col 70}{space 3}0.893{col 78}{space 4} .7437616{col 91}{space 3} 1.404634
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.040475{col 50}{space 2} .1630491{col 61}{space 1}    0.25{col 70}{space 3}0.800{col 78}{space 4} .7652477{col 91}{space 3}  1.41469
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.102447{col 50}{space 2} .1749302{col 61}{space 1}    0.61{col 70}{space 3}0.539{col 78}{space 4} .8077049{col 91}{space 3} 1.504746
{txt}{space 36} {c |}
{space 23}std_RTI_Autor {c |}{col 38}{res}{space 2} 1.206414{col 50}{space 2} .0995636{col 61}{space 1}    2.27{col 70}{space 3}0.023{col 78}{space 4} 1.026187{col 91}{space 3} 1.418294
{txt}{space 9}std_offshorable_Blinder2007 {c |}{col 38}{res}{space 2}  1.14407{col 50}{space 2} .0546372{col 61}{space 1}    2.82{col 70}{space 3}0.005{col 78}{space 4} 1.041812{col 91}{space 3} 1.256365
{txt}{space 31}_cons {c |}{col 38}{res}{space 2} .3878625{col 50}{space 2} .0860139{col 61}{space 1}   -4.27{col 70}{space 3}0.000{col 78}{space 4} .2511044{col 91}{space 3} .5991026
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. generate infullmodel_TableA1 = e(sample) 
{txt}
{com}.          
. svy: logit AI_worsen aiexposure_Webb2020 $controls_demographics  $controls_occupindices, or
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,091
{txt}{col 1}Number of PSUs{col 20}= {res}    3,091{txt}{col 49}Population size{col 67}={res} 3,213.0191
{txt}{col 49}Design df{col 67}= {res}     3,090
{txt}{col 49}F({res}  15{txt},{res}   3076{txt}){col 67}= {res}      5.27
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}aiexposure_Webb2020 {c |}{col 38}{res}{space 2} 1.012363{col 50}{space 2} .0523429{col 61}{space 1}    0.24{col 70}{space 3}0.812{col 78}{space 4} .9147633{col 91}{space 3} 1.120376
{txt}{space 33}age {c |}{col 38}{res}{space 2} .9945721{col 50}{space 2} .0040903{col 61}{space 1}   -1.32{col 70}{space 3}0.186{col 78}{space 4} .9865844{col 91}{space 3} 1.002624
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.044007{col 50}{space 2} .1072209{col 61}{space 1}    0.42{col 70}{space 3}0.675{col 78}{space 4}  .853591{col 91}{space 3} 1.276901
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  1.41115{col 50}{space 2}  .144833{col 61}{space 1}    3.36{col 70}{space 3}0.001{col 78}{space 4} 1.153921{col 91}{space 3}  1.72572
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} .7994449{col 50}{space 2} .0918147{col 61}{space 1}   -1.95{col 70}{space 3}0.051{col 78}{space 4} .6382508{col 91}{space 3}  1.00135
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.125451{col 50}{space 2}   .12907{col 61}{space 1}    1.03{col 70}{space 3}0.303{col 78}{space 4} .8988148{col 91}{space 3} 1.409235
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.020552{col 50}{space 2} .2003771{col 61}{space 1}    0.10{col 70}{space 3}0.917{col 78}{space 4} .6944536{col 91}{space 3} 1.499779
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9981597{col 50}{space 2}  .161428{col 61}{space 1}   -0.01{col 70}{space 3}0.991{col 78}{space 4} .7269173{col 91}{space 3} 1.370614
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2}  .776469{col 50}{space 2} .2552772{col 61}{space 1}   -0.77{col 70}{space 3}0.442{col 78}{space 4} .4075377{col 91}{space 3} 1.479383
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}  1.09588{col 50}{space 2} .1957191{col 61}{space 1}    0.51{col 70}{space 3}0.608{col 78}{space 4} .7721166{col 91}{space 3} 1.555404
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.021549{col 50}{space 2} .1775021{col 61}{space 1}    0.12{col 70}{space 3}0.902{col 78}{space 4} .7266053{col 91}{space 3} 1.436217
{txt}{space 34}4  {c |}{col 38}{res}{space 2}  1.07644{col 50}{space 2} .1822082{col 61}{space 1}    0.44{col 70}{space 3}0.663{col 78}{space 4} .7724157{col 91}{space 3} 1.500129
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.183178{col 50}{space 2} .2053927{col 61}{space 1}    0.97{col 70}{space 3}0.333{col 78}{space 4} .8418382{col 91}{space 3} 1.662921
{txt}{space 36} {c |}
{space 23}std_RTI_Autor {c |}{col 38}{res}{space 2} 1.352281{col 50}{space 2} .1193298{col 61}{space 1}    3.42{col 70}{space 3}0.001{col 78}{space 4}  1.13743{col 91}{space 3} 1.607716
{txt}{space 9}std_offshorable_Blinder2007 {c |}{col 38}{res}{space 2} 1.280871{col 50}{space 2} .0666357{col 61}{space 1}    4.76{col 70}{space 3}0.000{col 78}{space 4} 1.156659{col 91}{space 3} 1.418422
{txt}{space 31}_cons {c |}{col 38}{res}{space 2} .3347878{col 50}{space 2} .0808502{col 61}{space 1}   -4.53{col 70}{space 3}0.000{col 78}{space 4} .2085099{col 91}{space 3} .5375422
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. generate infullmodel_TableA2 = e(sample) 
{txt}
{com}.                  
. ******* TABLE A1 ******
. 
. ** Descriptives
. svy: tab aiexposure_groupschendstok2024 jobimpact2_AI, row stubw(40)
{txt}(running {bf:tabulate} on estimation sample)
{res}
{txt}{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     4,078
{txt}{col 1}Number of PSUs{col 20}= {res}    4,078{txt}{col 49}Population size{col 67}={res}  4,218.972
{txt}{col 49}Design df{col 67}= {res}     4,077

{txt}{hline 33}{c TT}{hline 49}
Exposure to AI: Std. Dev Groups  {c |}    Job Impact: Artificial Intelligence (4 Cat   
Based on Schendstok and Wertz    {c |}                     Version)                    
(2024) - For descr               {c |} No Impac    Worsen   Improve  Don't Kn     Total
{hline 33}{c +}{hline 49}
Least Exposed (>1 SD Below Mean) {c |}    {res}.6067     .1329     .0468     .2136         1
{txt}Less Exposed (0-1 SD Below Mean) {c |}    {res}.4856     .2324     .1108     .1712         1
{txt}More Exposed (0-1 SD Above Mean) {c |}    {res}.4236     .2569     .1105      .209         1
 {txt}Most Exposed (>1 SD Above Mean) {c |}    {res}.4032     .3168     .0952     .1848         1
                                 {txt}{c |} 
                           Total {c |}    {res}.4777     .2337     .0956     .1931         1
{txt}{hline 33}{c BT}{hline 49}
  Key:  {col 1}{res}row proportion

{txt}  Pearson:
{col 5}Uncorrected{col 19}chi2({res}9{txt}){col 35}= {res} 133.5845
{txt}{col 5}Design-based{col 19}F({res}8.97{txt}, {res}36587.66{txt}){col 35}= {res}  12.4468{col 51}{txt}P = {res}0.0000
{txt}
{com}. 
. ** Model 1a
. svy: logit AI_worsen aiexposure_schendstok2024 if infullmodel_TableA1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}   1{txt},{res}   3961{txt}){col 67}= {res}     63.89
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}  Linearized
{col 1}                AI_worsen{col 27}{c |} Odds Ratio{col 39}   Std. Err.{col 51}      t{col 59}   P>|t|{col 67}     [95% Con{col 80}f. Interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
aiexposure_schendstok2024 {c |}{col 27}{res}{space 2} 1.367037{col 39}{space 2} .0534725{col 50}{space 1}    7.99{col 59}{space 3}0.000{col 67}{space 4}  1.26612{col 80}{space 3} 1.475998
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .3059848{col 39}{space 2} .0126316{col 50}{space 1}  -28.69{col 59}{space 3}0.000{col 67}{space 4} .2821955{col 80}{space 3} .3317796
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 1b
. svy: logit AI_worsen aiexposure_schendstok2024 $controls_demographics if infullmodel_TableA1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}  13{txt},{res}   3949{txt}){col 67}= {res}      6.78
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}aiexposure_schendstok2024 {c |}{col 38}{res}{space 2} 1.361036{col 50}{space 2}  .055365{col 61}{space 1}    7.58{col 70}{space 3}0.000{col 78}{space 4} 1.256705{col 91}{space 3} 1.474028
{txt}{space 33}age {c |}{col 38}{res}{space 2} .9929058{col 50}{space 2} .0036075{col 61}{space 1}   -1.96{col 70}{space 3}0.050{col 78}{space 4} .9858583{col 91}{space 3} 1.000004
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.133896{col 50}{space 2} .0978371{col 61}{space 1}    1.46{col 70}{space 3}0.145{col 78}{space 4} .9574273{col 91}{space 3} 1.342891
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.301246{col 50}{space 2}  .115008{col 61}{space 1}    2.98{col 70}{space 3}0.003{col 78}{space 4}  1.09422{col 91}{space 3} 1.547441
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} .7689302{col 50}{space 2} .0804489{col 61}{space 1}   -2.51{col 70}{space 3}0.012{col 78}{space 4}   .62633{col 91}{space 3} .9439969
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9262152{col 50}{space 2} .0875127{col 61}{space 1}   -0.81{col 70}{space 3}0.417{col 78}{space 4} .7695949{col 91}{space 3} 1.114709
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9117301{col 50}{space 2} .1632339{col 61}{space 1}   -0.52{col 70}{space 3}0.606{col 78}{space 4} .6418339{col 91}{space 3}  1.29512
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9798368{col 50}{space 2} .1414144{col 61}{space 1}   -0.14{col 70}{space 3}0.888{col 78}{space 4} .7383581{col 91}{space 3} 1.300291
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8221795{col 50}{space 2} .2323396{col 61}{space 1}   -0.69{col 70}{space 3}0.488{col 78}{space 4}  .472446{col 91}{space 3} 1.430807
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.110497{col 50}{space 2} .1846423{col 61}{space 1}    0.63{col 70}{space 3}0.529{col 78}{space 4} .8015766{col 91}{space 3} 1.538473
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.002059{col 50}{space 2}  .161746{col 61}{space 1}    0.01{col 70}{space 3}0.990{col 78}{space 4} .7302238{col 91}{space 3}  1.37509
{txt}{space 34}4  {c |}{col 38}{res}{space 2}  1.01438{col 50}{space 2} .1577046{col 61}{space 1}    0.09{col 70}{space 3}0.927{col 78}{space 4}  .747868{col 91}{space 3} 1.375867
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.076963{col 50}{space 2} .1682556{col 61}{space 1}    0.47{col 70}{space 3}0.635{col 78}{space 4} .7928214{col 91}{space 3} 1.462938
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .4187505{col 50}{space 2} .0926231{col 61}{space 1}   -3.94{col 70}{space 3}0.000{col 78}{space 4} .2714071{col 91}{space 3} .6460846
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 1c
. svy: logit AI_worsen aiexposure_schendstok2024 $controls_demographics $controls_occupindices if infullmodel_TableA1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}  15{txt},{res}   3947{txt}){col 67}= {res}      7.21
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}aiexposure_schendstok2024 {c |}{col 38}{res}{space 2} 1.254052{col 50}{space 2} .0565886{col 61}{space 1}    5.02{col 70}{space 3}0.000{col 78}{space 4} 1.147872{col 91}{space 3} 1.370053
{txt}{space 33}age {c |}{col 38}{res}{space 2} .9931302{col 50}{space 2} .0036252{col 61}{space 1}   -1.89{col 70}{space 3}0.059{col 78}{space 4} .9860482{col 91}{space 3} 1.000263
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.105329{col 50}{space 2}  .096108{col 61}{space 1}    1.15{col 70}{space 3}0.249{col 78}{space 4} .9320891{col 91}{space 3} 1.310768
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.334116{col 50}{space 2} .1193482{col 61}{space 1}    3.22{col 70}{space 3}0.001{col 78}{space 4} 1.119497{col 91}{space 3} 1.589879
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} .7745197{col 50}{space 2} .0814953{col 61}{space 1}   -2.43{col 70}{space 3}0.015{col 78}{space 4} .6301462{col 91}{space 3} .9519707
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.029998{col 50}{space 2} .1004884{col 61}{space 1}    0.30{col 70}{space 3}0.762{col 78}{space 4} .8506798{col 91}{space 3} 1.247115
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9674227{col 50}{space 2} .1731842{col 61}{space 1}   -0.19{col 70}{space 3}0.853{col 78}{space 4} .6810688{col 91}{space 3} 1.374174
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.049129{col 50}{space 2} .1510132{col 61}{space 1}    0.33{col 70}{space 3}0.739{col 78}{space 4} .7911667{col 91}{space 3}   1.3912
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8510905{col 50}{space 2} .2437038{col 61}{space 1}   -0.56{col 70}{space 3}0.573{col 78}{space 4} .4854736{col 91}{space 3} 1.492059
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.102863{col 50}{space 2} .1839508{col 61}{space 1}    0.59{col 70}{space 3}0.557{col 78}{space 4} .7952484{col 91}{space 3} 1.529467
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.022112{col 50}{space 2} .1657357{col 61}{space 1}    0.13{col 70}{space 3}0.893{col 78}{space 4} .7437616{col 91}{space 3} 1.404634
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.040475{col 50}{space 2} .1630491{col 61}{space 1}    0.25{col 70}{space 3}0.800{col 78}{space 4} .7652477{col 91}{space 3}  1.41469
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.102447{col 50}{space 2} .1749302{col 61}{space 1}    0.61{col 70}{space 3}0.539{col 78}{space 4} .8077049{col 91}{space 3} 1.504746
{txt}{space 36} {c |}
{space 23}std_RTI_Autor {c |}{col 38}{res}{space 2} 1.206414{col 50}{space 2} .0995636{col 61}{space 1}    2.27{col 70}{space 3}0.023{col 78}{space 4} 1.026187{col 91}{space 3} 1.418294
{txt}{space 9}std_offshorable_Blinder2007 {c |}{col 38}{res}{space 2}  1.14407{col 50}{space 2} .0546372{col 61}{space 1}    2.82{col 70}{space 3}0.005{col 78}{space 4} 1.041812{col 91}{space 3} 1.256365
{txt}{space 31}_cons {c |}{col 38}{res}{space 2} .3878625{col 50}{space 2} .0860139{col 61}{space 1}   -4.27{col 70}{space 3}0.000{col 78}{space 4} .2511044{col 91}{space 3} .5991026
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. ** Model 2a
. svy: logit AI_improve aiexposure_schendstok2024 if infullmodel_TableA1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}   1{txt},{res}   3961{txt}){col 67}= {res}      5.64
{txt}{col 49}Prob > F{col 67}= {res}    0.0176

{txt}{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}  Linearized
{col 1}               AI_improve{col 27}{c |} Odds Ratio{col 39}   Std. Err.{col 51}      t{col 59}   P>|t|{col 67}     [95% Con{col 80}f. Interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
aiexposure_schendstok2024 {c |}{col 27}{res}{space 2} 1.137004{col 39}{space 2} .0614505{col 50}{space 1}    2.38{col 59}{space 3}0.018{col 67}{space 4}  1.02269{col 80}{space 3} 1.264096
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .1034156{col 39}{space 2} .0062281{col 50}{space 1}  -37.68{col 59}{space 3}0.000{col 67}{space 4} .0918983{col 80}{space 3} .1163764
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 2b
. svy: logit AI_improve aiexposure_schendstok2024 $controls_demographics if infullmodel_TableA1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}  13{txt},{res}   3949{txt}){col 67}= {res}     11.90
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                          AI_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}aiexposure_schendstok2024 {c |}{col 38}{res}{space 2}  1.05111{col 50}{space 2} .0680839{col 61}{space 1}    0.77{col 70}{space 3}0.442{col 78}{space 4} .9257553{col 91}{space 3} 1.193439
{txt}{space 33}age {c |}{col 38}{res}{space 2} .9688018{col 50}{space 2} .0055137{col 61}{space 1}   -5.57{col 70}{space 3}0.000{col 78}{space 4} .9580519{col 91}{space 3} .9796723
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.657732{col 50}{space 2}  .210802{col 61}{space 1}    3.97{col 70}{space 3}0.000{col 78}{space 4} 1.291933{col 91}{space 3} 2.127104
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 2.012859{col 50}{space 2} .2782163{col 61}{space 1}    5.06{col 70}{space 3}0.000{col 78}{space 4} 1.535058{col 91}{space 3} 2.639381
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} 2.233673{col 50}{space 2} .4548963{col 61}{space 1}    3.95{col 70}{space 3}0.000{col 78}{space 4} 1.498359{col 91}{space 3} 3.329838
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .7289735{col 50}{space 2} .1047091{col 61}{space 1}   -2.20{col 70}{space 3}0.028{col 78}{space 4} .5500581{col 91}{space 3} .9660842
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .7100926{col 50}{space 2} .1993503{col 61}{space 1}   -1.22{col 70}{space 3}0.223{col 78}{space 4} .4095218{col 91}{space 3} 1.231269
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.141853{col 50}{space 2}  .256607{col 61}{space 1}    0.59{col 70}{space 3}0.555{col 78}{space 4} .7349577{col 91}{space 3} 1.774018
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2}  1.06018{col 50}{space 2}   .53728{col 61}{space 1}    0.12{col 70}{space 3}0.908{col 78}{space 4} .3925314{col 91}{space 3} 2.863419
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5194301{col 50}{space 2} .1538813{col 61}{space 1}   -2.21{col 70}{space 3}0.027{col 78}{space 4} .2905905{col 91}{space 3} .9284807
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .4769349{col 50}{space 2} .1295693{col 61}{space 1}   -2.73{col 70}{space 3}0.006{col 78}{space 4} .2799895{col 91}{space 3} .8124121
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .5888492{col 50}{space 2} .1428525{col 61}{space 1}   -2.18{col 70}{space 3}0.029{col 78}{space 4} .3659677{col 91}{space 3} .9474696
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .8733216{col 50}{space 2} .2028622{col 61}{space 1}   -0.58{col 70}{space 3}0.560{col 78}{space 4}  .553847{col 91}{space 3} 1.377078
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .1640927{col 50}{space 2} .0609071{col 61}{space 1}   -4.87{col 70}{space 3}0.000{col 78}{space 4} .0792589{col 91}{space 3} .3397274
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 2c
. svy: logit AI_improve aiexposure_schendstok2024 $controls_demographics $controls_occupindices if infullmodel_TableA1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}  15{txt},{res}   3947{txt}){col 67}= {res}     10.74
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                          AI_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}aiexposure_schendstok2024 {c |}{col 38}{res}{space 2} 1.022281{col 50}{space 2} .0754651{col 61}{space 1}    0.30{col 70}{space 3}0.765{col 78}{space 4} .8845347{col 91}{space 3} 1.181477
{txt}{space 33}age {c |}{col 38}{res}{space 2} .9686076{col 50}{space 2} .0055361{col 61}{space 1}   -5.58{col 70}{space 3}0.000{col 78}{space 4} .9578143{col 91}{space 3} .9795226
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.622326{col 50}{space 2} .2091389{col 61}{space 1}    3.75{col 70}{space 3}0.000{col 78}{space 4} 1.260009{col 91}{space 3} 2.088828
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  1.85505{col 50}{space 2} .2628688{col 61}{space 1}    4.36{col 70}{space 3}0.000{col 78}{space 4} 1.405076{col 91}{space 3} 2.449128
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} 2.067502{col 50}{space 2} .4220846{col 61}{space 1}    3.56{col 70}{space 3}0.000{col 78}{space 4} 1.385537{col 91}{space 3}  3.08513
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .7625231{col 50}{space 2} .1168318{col 61}{space 1}   -1.77{col 70}{space 3}0.077{col 78}{space 4} .5646693{col 91}{space 3} 1.029703
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .7217036{col 50}{space 2}  .205662{col 61}{space 1}   -1.14{col 70}{space 3}0.252{col 78}{space 4} .4127813{col 91}{space 3} 1.261821
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.110234{col 50}{space 2} .2501187{col 61}{space 1}    0.46{col 70}{space 3}0.643{col 78}{space 4} .7138277{col 91}{space 3} 1.726776
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.151525{col 50}{space 2} .5863161{col 61}{space 1}    0.28{col 70}{space 3}0.782{col 78}{space 4} .4243647{col 91}{space 3} 3.124696
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5120135{col 50}{space 2} .1516725{col 61}{space 1}   -2.26{col 70}{space 3}0.024{col 78}{space 4}  .286454{col 91}{space 3} .9151829
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .4567141{col 50}{space 2} .1243694{col 61}{space 1}   -2.88{col 70}{space 3}0.004{col 78}{space 4} .2677811{col 91}{space 3} .7789489
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .5534263{col 50}{space 2} .1363225{col 61}{space 1}   -2.40{col 70}{space 3}0.016{col 78}{space 4} .3414474{col 91}{space 3} .8970069
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .7602791{col 50}{space 2} .1787773{col 61}{space 1}   -1.17{col 70}{space 3}0.244{col 78}{space 4} .4794623{col 91}{space 3} 1.205568
{txt}{space 36} {c |}
{space 23}std_RTI_Autor {c |}{col 38}{res}{space 2} .6845765{col 50}{space 2}   .10066{col 61}{space 1}   -2.58{col 70}{space 3}0.010{col 78}{space 4} .5131251{col 91}{space 3} .9133153
{txt}{space 9}std_offshorable_Blinder2007 {c |}{col 38}{res}{space 2} 1.231019{col 50}{space 2}  .080662{col 61}{space 1}    3.17{col 70}{space 3}0.002{col 78}{space 4} 1.082612{col 91}{space 3} 1.399769
{txt}{space 31}_cons {c |}{col 38}{res}{space 2} .1847117{col 50}{space 2} .0696466{col 61}{space 1}   -4.48{col 70}{space 3}0.000{col 78}{space 4} .0881953{col 91}{space 3} .3868504
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. ** Model 3a
. svy: logit AI_directionworse aiexposure_schendstok2024 if infullmodel_TableA1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}   1{txt},{res}   1321{txt}){col 67}= {res}      3.78
{txt}{col 49}Prob > F{col 67}= {res}    0.0521

{txt}{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}  Linearized
{col 1}        AI_directionworse{col 27}{c |} Odds Ratio{col 39}   Std. Err.{col 51}      t{col 59}   P>|t|{col 67}     [95% Con{col 80}f. Interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
aiexposure_schendstok2024 {c |}{col 27}{res}{space 2}  1.14666{col 39}{space 2} .0807074{col 50}{space 1}    1.94{col 59}{space 3}0.052{col 67}{space 4} .9987758{col 80}{space 3}  1.31644
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} 2.474166{col 39}{space 2} .1683403{col 50}{space 1}   13.31{col 59}{space 3}0.000{col 67}{space 4} 2.165014{col 80}{space 3} 2.827463
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 3b
. svy: logit AI_directionworse aiexposure_schendstok2024 $controls_demographics if infullmodel_TableA1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}  13{txt},{res}   1309{txt}){col 67}= {res}      5.66
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                   AI_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}aiexposure_schendstok2024 {c |}{col 38}{res}{space 2} 1.134391{col 50}{space 2} .0847973{col 61}{space 1}    1.69{col 70}{space 3}0.092{col 78}{space 4} .9796616{col 91}{space 3} 1.313559
{txt}{space 33}age {c |}{col 38}{res}{space 2} 1.021979{col 50}{space 2} .0062855{col 61}{space 1}    3.53{col 70}{space 3}0.000{col 78}{space 4} 1.009722{col 91}{space 3} 1.034384
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} .7213245{col 50}{space 2} .1049359{col 61}{space 1}   -2.25{col 70}{space 3}0.025{col 78}{space 4} .5422345{col 91}{space 3} .9595645
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  .659864{col 50}{space 2} .1000061{col 61}{space 1}   -2.74{col 70}{space 3}0.006{col 78}{space 4} .4901532{col 91}{space 3} .8883357
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} .4023393{col 50}{space 2} .0904495{col 61}{space 1}   -4.05{col 70}{space 3}0.000{col 78}{space 4} .2588566{col 91}{space 3} .6253535
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.249304{col 50}{space 2} .2029933{col 61}{space 1}    1.37{col 70}{space 3}0.171{col 78}{space 4} .9083088{col 91}{space 3} 1.718314
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.245778{col 50}{space 2} .4027023{col 61}{space 1}    0.68{col 70}{space 3}0.497{col 78}{space 4} .6607458{col 91}{space 3} 2.348805
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9053343{col 50}{space 2} .2412264{col 61}{space 1}   -0.37{col 70}{space 3}0.709{col 78}{space 4} .5367824{col 91}{space 3} 1.526932
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .7529367{col 50}{space 2} .4222511{col 61}{space 1}   -0.51{col 70}{space 3}0.613{col 78}{space 4}  .250589{col 91}{space 3} 2.262324
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.797254{col 50}{space 2} .6121956{col 61}{space 1}    1.72{col 70}{space 3}0.085{col 78}{space 4} .9212981{col 91}{space 3} 3.506055
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.930246{col 50}{space 2} .5980521{col 61}{space 1}    2.12{col 70}{space 3}0.034{col 78}{space 4} 1.051093{col 91}{space 3} 3.544737
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.573254{col 50}{space 2} .4437966{col 61}{space 1}    1.61{col 70}{space 3}0.108{col 78}{space 4} .9046167{col 91}{space 3} 2.736107
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.165721{col 50}{space 2} .3162036{col 61}{space 1}    0.57{col 70}{space 3}0.572{col 78}{space 4} .6846888{col 91}{space 3} 1.984706
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 2.245371{col 50}{space 2} .8680993{col 61}{space 1}    2.09{col 70}{space 3}0.037{col 78}{space 4} 1.051712{col 91}{space 3} 4.793792
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 3c
. svy: logit AI_directionworse aiexposure_schendstok2024 $controls_demographics $controls_occupindices if infullmodel_TableA1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}  15{txt},{res}   1307{txt}){col 67}= {res}      5.28
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                   AI_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}aiexposure_schendstok2024 {c |}{col 38}{res}{space 2} 1.110422{col 50}{space 2} .0910467{col 61}{space 1}    1.28{col 70}{space 3}0.202{col 78}{space 4} .9454345{col 91}{space 3} 1.304201
{txt}{space 33}age {c |}{col 38}{res}{space 2} 1.022897{col 50}{space 2} .0064016{col 61}{space 1}    3.62{col 70}{space 3}0.000{col 78}{space 4} 1.010415{col 91}{space 3} 1.035533
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} .7382118{col 50}{space 2} .1076829{col 61}{space 1}   -2.08{col 70}{space 3}0.038{col 78}{space 4} .5545011{col 91}{space 3} .9827874
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7140073{col 50}{space 2} .1112775{col 61}{space 1}   -2.16{col 70}{space 3}0.031{col 78}{space 4} .5259227{col 91}{space 3} .9693563
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2}  .437253{col 50}{space 2}  .099051{col 61}{space 1}   -3.65{col 70}{space 3}0.000{col 78}{space 4} .2803709{col 91}{space 3} .6819186
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.287309{col 50}{space 2} .2213372{col 61}{space 1}    1.47{col 70}{space 3}0.142{col 78}{space 4} .9187446{col 91}{space 3} 1.803727
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}  1.24505{col 50}{space 2} .4021416{col 61}{space 1}    0.68{col 70}{space 3}0.498{col 78}{space 4} .6606986{col 91}{space 3}  2.34623
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.010015{col 50}{space 2} .2681432{col 61}{space 1}    0.04{col 70}{space 3}0.970{col 78}{space 4} .5999841{col 91}{space 3} 1.700261
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .7285478{col 50}{space 2}  .405184{col 61}{space 1}   -0.57{col 70}{space 3}0.569{col 78}{space 4} .2446953{col 91}{space 3} 2.169154
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.789866{col 50}{space 2} .6034078{col 61}{space 1}    1.73{col 70}{space 3}0.084{col 78}{space 4} .9238392{col 91}{space 3} 3.467725
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.998065{col 50}{space 2} .6142803{col 61}{space 1}    2.25{col 70}{space 3}0.025{col 78}{space 4} 1.093146{col 91}{space 3} 3.652085
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.683955{col 50}{space 2} .4766284{col 61}{space 1}    1.84{col 70}{space 3}0.066{col 78}{space 4} .9664617{col 91}{space 3}  2.93411
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.306098{col 50}{space 2} .3518106{col 61}{space 1}    0.99{col 70}{space 3}0.322{col 78}{space 4} .7699918{col 91}{space 3} 2.215469
{txt}{space 36} {c |}
{space 23}std_RTI_Autor {c |}{col 38}{res}{space 2} 1.565688{col 50}{space 2} .2556491{col 61}{space 1}    2.75{col 70}{space 3}0.006{col 78}{space 4} 1.136558{col 91}{space 3} 2.156844
{txt}{space 9}std_offshorable_Blinder2007 {c |}{col 38}{res}{space 2} .9220792{col 50}{space 2} .0678739{col 61}{space 1}   -1.10{col 70}{space 3}0.271{col 78}{space 4} .7980942{col 91}{space 3} 1.065325
{txt}{space 31}_cons {c |}{col 38}{res}{space 2}  1.86178{col 50}{space 2} .7432452{col 61}{space 1}    1.56{col 70}{space 3}0.120{col 78}{space 4} .8507592{col 91}{space 3} 4.074272
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. 
. ******* TABLE A2 ******
. 
. ** Descriptives
. svy: tab aiexposure_groupWebb2020 jobimpact2_AI, row stubw(40)
{txt}(running {bf:tabulate} on estimation sample)
{res}
{txt}{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,178
{txt}{col 1}Number of PSUs{col 20}= {res}    3,178{txt}{col 49}Population size{col 67}={res} 3,311.2586
{txt}{col 49}Design df{col 67}= {res}     3,177

{txt}{hline 33}{c TT}{hline 49}
Exposure to AI: Std. Dev Groups  {c |}    Job Impact: Artificial Intelligence (4 Cat   
Based on Webb (2020) - For       {c |}                     Version)                    
descriptives                     {c |} No Impac    Worsen   Improve  Don't Kn     Total
{hline 33}{c +}{hline 49}
Least Exposed (>1 SD Below Mean) {c |}    {res}.5231     .2133     .0411     .2225         1
{txt}Less Exposed (0-1 SD Below Mean) {c |}    {res}.4676     .2353     .0881      .209         1
{txt}More Exposed (0-1 SD Above Mean) {c |}    {res}.4964     .2167     .0987     .1882         1
 {txt}Most Exposed (>1 SD Above Mean) {c |}    {res}.4427     .2569     .1274      .173         1
                                 {txt}{c |} 
                           Total {c |}    {res}.4805     .2299       .09     .1996         1
{txt}{hline 33}{c BT}{hline 49}
  Key:  {col 1}{res}row proportion

{txt}  Pearson:
{col 5}Uncorrected{col 19}chi2({res}9{txt}){col 35}= {res}  32.1588
{txt}{col 5}Design-based{col 19}F({res}8.91{txt}, {res}28318.41{txt}){col 35}= {res}   2.8252{col 51}{txt}P = {res}0.0026
{txt}
{com}. 
. ** Model 1a
. svy: logit AI_worsen aiexposure_Webb2020 if infullmodel_TableA2 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,091
{txt}{col 1}Number of PSUs{col 20}= {res}    3,091{txt}{col 49}Population size{col 67}={res} 3,213.0191
{txt}{col 49}Design df{col 67}= {res}     3,090
{txt}{col 49}F({res}   1{txt},{res}   3090{txt}){col 67}= {res}      4.03
{txt}{col 49}Prob > F{col 67}= {res}    0.0449

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}  Linearized
{col 1}          AI_worsen{col 21}{c |} Odds Ratio{col 33}   Std. Err.{col 45}      t{col 53}   P>|t|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
aiexposure_Webb2020 {c |}{col 21}{res}{space 2} 1.095414{col 33}{space 2} .0497435{col 44}{space 1}    2.01{col 53}{space 3}0.045{col 61}{space 4} 1.002096{col 74}{space 3} 1.197421
{txt}{space 14}_cons {c |}{col 21}{res}{space 2} .3015029{col 33}{space 2} .0139314{col 44}{space 1}  -25.95{col 53}{space 3}0.000{col 61}{space 4}  .275388{col 74}{space 3} .3300944
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 1b
. svy: logit AI_worsen aiexposure_Webb2020 $controls_demographics if infullmodel_TableA2 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,091
{txt}{col 1}Number of PSUs{col 20}= {res}    3,091{txt}{col 49}Population size{col 67}={res} 3,213.0191
{txt}{col 49}Design df{col 67}= {res}     3,090
{txt}{col 49}F({res}  13{txt},{res}   3078{txt}){col 67}= {res}      2.13
{txt}{col 49}Prob > F{col 67}= {res}    0.0103

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}aiexposure_Webb2020 {c |}{col 38}{res}{space 2} 1.068694{col 50}{space 2} .0524595{col 61}{space 1}    1.35{col 70}{space 3}0.176{col 78}{space 4} .9706298{col 91}{space 3} 1.176666
{txt}{space 33}age {c |}{col 38}{res}{space 2} .9939962{col 50}{space 2} .0040345{col 61}{space 1}   -1.48{col 70}{space 3}0.138{col 78}{space 4} .9861171{col 91}{space 3} 1.001938
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.062218{col 50}{space 2} .1082077{col 61}{space 1}    0.59{col 70}{space 3}0.554{col 78}{space 4} .8698977{col 91}{space 3} 1.297058
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.383553{col 50}{space 2} .1388852{col 61}{space 1}    3.23{col 70}{space 3}0.001{col 78}{space 4}  1.13636{col 91}{space 3} 1.684517
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2}  .801855{col 50}{space 2} .0908733{col 61}{space 1}   -1.95{col 70}{space 3}0.051{col 78}{space 4} .6420847{col 91}{space 3} 1.001381
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9581451{col 50}{space 2} .1057451{col 61}{space 1}   -0.39{col 70}{space 3}0.698{col 78}{space 4} .7717064{col 91}{space 3} 1.189626
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9376328{col 50}{space 2} .1875745{col 61}{space 1}   -0.32{col 70}{space 3}0.748{col 78}{space 4} .6334061{col 91}{space 3} 1.387981
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .8669066{col 50}{space 2} .1397393{col 61}{space 1}   -0.89{col 70}{space 3}0.376{col 78}{space 4} .6319908{col 91}{space 3} 1.189142
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .7076522{col 50}{space 2} .2313059{col 61}{space 1}   -1.06{col 70}{space 3}0.290{col 78}{space 4} .3728068{col 91}{space 3} 1.343247
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.142871{col 50}{space 2} .2016868{col 61}{space 1}    0.76{col 70}{space 3}0.449{col 78}{space 4} .8085808{col 91}{space 3} 1.615365
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.025547{col 50}{space 2} .1766165{col 61}{space 1}    0.15{col 70}{space 3}0.884{col 78}{space 4}  .731656{col 91}{space 3} 1.437488
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.064462{col 50}{space 2} .1771872{col 61}{space 1}    0.38{col 70}{space 3}0.707{col 78}{space 4} .7680438{col 91}{space 3} 1.475279
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.190358{col 50}{space 2} .2015555{col 61}{space 1}    1.03{col 70}{space 3}0.304{col 78}{space 4} .8540683{col 91}{space 3} 1.659061
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .3677865{col 50}{space 2} .0879534{col 61}{space 1}   -4.18{col 70}{space 3}0.000{col 78}{space 4} .2301217{col 91}{space 3}  .587806
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 1c
. svy: logit AI_worsen aiexposure_Webb2020 $controls_demographics $controls_occupindices if infullmodel_TableA2 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,091
{txt}{col 1}Number of PSUs{col 20}= {res}    3,091{txt}{col 49}Population size{col 67}={res} 3,213.0191
{txt}{col 49}Design df{col 67}= {res}     3,090
{txt}{col 49}F({res}  15{txt},{res}   3076{txt}){col 67}= {res}      5.27
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}aiexposure_Webb2020 {c |}{col 38}{res}{space 2} 1.012363{col 50}{space 2} .0523429{col 61}{space 1}    0.24{col 70}{space 3}0.812{col 78}{space 4} .9147633{col 91}{space 3} 1.120376
{txt}{space 33}age {c |}{col 38}{res}{space 2} .9945721{col 50}{space 2} .0040903{col 61}{space 1}   -1.32{col 70}{space 3}0.186{col 78}{space 4} .9865844{col 91}{space 3} 1.002624
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.044007{col 50}{space 2} .1072209{col 61}{space 1}    0.42{col 70}{space 3}0.675{col 78}{space 4}  .853591{col 91}{space 3} 1.276901
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  1.41115{col 50}{space 2}  .144833{col 61}{space 1}    3.36{col 70}{space 3}0.001{col 78}{space 4} 1.153921{col 91}{space 3}  1.72572
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} .7994449{col 50}{space 2} .0918147{col 61}{space 1}   -1.95{col 70}{space 3}0.051{col 78}{space 4} .6382508{col 91}{space 3}  1.00135
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.125451{col 50}{space 2}   .12907{col 61}{space 1}    1.03{col 70}{space 3}0.303{col 78}{space 4} .8988148{col 91}{space 3} 1.409235
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.020552{col 50}{space 2} .2003771{col 61}{space 1}    0.10{col 70}{space 3}0.917{col 78}{space 4} .6944536{col 91}{space 3} 1.499779
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9981597{col 50}{space 2}  .161428{col 61}{space 1}   -0.01{col 70}{space 3}0.991{col 78}{space 4} .7269173{col 91}{space 3} 1.370614
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2}  .776469{col 50}{space 2} .2552772{col 61}{space 1}   -0.77{col 70}{space 3}0.442{col 78}{space 4} .4075377{col 91}{space 3} 1.479383
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}  1.09588{col 50}{space 2} .1957191{col 61}{space 1}    0.51{col 70}{space 3}0.608{col 78}{space 4} .7721166{col 91}{space 3} 1.555404
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.021549{col 50}{space 2} .1775021{col 61}{space 1}    0.12{col 70}{space 3}0.902{col 78}{space 4} .7266053{col 91}{space 3} 1.436217
{txt}{space 34}4  {c |}{col 38}{res}{space 2}  1.07644{col 50}{space 2} .1822082{col 61}{space 1}    0.44{col 70}{space 3}0.663{col 78}{space 4} .7724157{col 91}{space 3} 1.500129
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.183178{col 50}{space 2} .2053927{col 61}{space 1}    0.97{col 70}{space 3}0.333{col 78}{space 4} .8418382{col 91}{space 3} 1.662921
{txt}{space 36} {c |}
{space 23}std_RTI_Autor {c |}{col 38}{res}{space 2} 1.352281{col 50}{space 2} .1193298{col 61}{space 1}    3.42{col 70}{space 3}0.001{col 78}{space 4}  1.13743{col 91}{space 3} 1.607716
{txt}{space 9}std_offshorable_Blinder2007 {c |}{col 38}{res}{space 2} 1.280871{col 50}{space 2} .0666357{col 61}{space 1}    4.76{col 70}{space 3}0.000{col 78}{space 4} 1.156659{col 91}{space 3} 1.418422
{txt}{space 31}_cons {c |}{col 38}{res}{space 2} .3347878{col 50}{space 2} .0808502{col 61}{space 1}   -4.53{col 70}{space 3}0.000{col 78}{space 4} .2085099{col 91}{space 3} .5375422
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. ** Model 2a
. svy: logit AI_improve aiexposure_Webb2020 if infullmodel_TableA2 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,091
{txt}{col 1}Number of PSUs{col 20}= {res}    3,091{txt}{col 49}Population size{col 67}={res} 3,213.0191
{txt}{col 49}Design df{col 67}= {res}     3,090
{txt}{col 49}F({res}   1{txt},{res}   3090{txt}){col 67}= {res}     26.18
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}  Linearized
{col 1}         AI_improve{col 21}{c |} Odds Ratio{col 33}   Std. Err.{col 45}      t{col 53}   P>|t|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
aiexposure_Webb2020 {c |}{col 21}{res}{space 2} 1.356947{col 33}{space 2} .0809511{col 44}{space 1}    5.12{col 53}{space 3}0.000{col 61}{space 4} 1.207155{col 74}{space 3} 1.525326
{txt}{space 14}_cons {c |}{col 21}{res}{space 2} .0911058{col 33}{space 2} .0066262{col 44}{space 1}  -32.94{col 53}{space 3}0.000{col 61}{space 4} .0789974{col 74}{space 3}   .10507
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 2b
. svy: logit AI_improve aiexposure_Webb2020 $controls_demographics if infullmodel_TableA2 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,091
{txt}{col 1}Number of PSUs{col 20}= {res}    3,091{txt}{col 49}Population size{col 67}={res} 3,213.0191
{txt}{col 49}Design df{col 67}= {res}     3,090
{txt}{col 49}F({res}  13{txt},{res}   3078{txt}){col 67}= {res}     11.13
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                          AI_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}aiexposure_Webb2020 {c |}{col 38}{res}{space 2} 1.131373{col 50}{space 2} .0803381{col 61}{space 1}    1.74{col 70}{space 3}0.082{col 78}{space 4} .9843256{col 91}{space 3} 1.300387
{txt}{space 33}age {c |}{col 38}{res}{space 2} .9597311{col 50}{space 2} .0064301{col 61}{space 1}   -6.13{col 70}{space 3}0.000{col 78}{space 4} .9472058{col 91}{space 3} .9724221
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.591837{col 50}{space 2} .2426811{col 61}{space 1}    3.05{col 70}{space 3}0.002{col 78}{space 4} 1.180535{col 91}{space 3} 2.146436
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 2.162845{col 50}{space 2} .3475299{col 61}{space 1}    4.80{col 70}{space 3}0.000{col 78}{space 4} 1.578335{col 91}{space 3} 2.963818
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} 2.893192{col 50}{space 2} .7323287{col 61}{space 1}    4.20{col 70}{space 3}0.000{col 78}{space 4} 1.761309{col 91}{space 3} 4.752466
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .7469041{col 50}{space 2} .1326659{col 61}{space 1}   -1.64{col 70}{space 3}0.100{col 78}{space 4} .5272472{col 91}{space 3} 1.058072
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .7276129{col 50}{space 2} .2406478{col 61}{space 1}   -0.96{col 70}{space 3}0.336{col 78}{space 4} .3804232{col 91}{space 3} 1.391662
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.059606{col 50}{space 2}  .312905{col 61}{space 1}    0.20{col 70}{space 3}0.845{col 78}{space 4} .5938593{col 91}{space 3} 1.890625
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.047184{col 50}{space 2} .5420792{col 61}{space 1}    0.09{col 70}{space 3}0.929{col 78}{space 4} .3795093{col 91}{space 3} 2.889507
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .4767647{col 50}{space 2} .1721634{col 61}{space 1}   -2.05{col 70}{space 3}0.040{col 78}{space 4} .2348595{col 91}{space 3} .9678321
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .3949641{col 50}{space 2} .1266572{col 61}{space 1}   -2.90{col 70}{space 3}0.004{col 78}{space 4} .2106139{col 91}{space 3} .7406758
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .6146086{col 50}{space 2} .1716071{col 61}{space 1}   -1.74{col 70}{space 3}0.081{col 78}{space 4}  .355499{col 91}{space 3} 1.062573
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2}  .824901{col 50}{space 2} .2220385{col 61}{space 1}   -0.72{col 70}{space 3}0.475{col 78}{space 4} .4866248{col 91}{space 3} 1.398329
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .1839301{col 50}{space 2} .0815614{col 61}{space 1}   -3.82{col 70}{space 3}0.000{col 78}{space 4} .0770994{col 91}{space 3} .4387881
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 2c
. svy: logit AI_improve aiexposure_Webb2020 $controls_demographics $controls_occupindices if infullmodel_TableA2 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,091
{txt}{col 1}Number of PSUs{col 20}= {res}    3,091{txt}{col 49}Population size{col 67}={res} 3,213.0191
{txt}{col 49}Design df{col 67}= {res}     3,090
{txt}{col 49}F({res}  15{txt},{res}   3076{txt}){col 67}= {res}      9.86
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                          AI_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}aiexposure_Webb2020 {c |}{col 38}{res}{space 2} 1.099565{col 50}{space 2} .0821238{col 61}{space 1}    1.27{col 70}{space 3}0.204{col 78}{space 4} .9497779{col 91}{space 3} 1.272976
{txt}{space 33}age {c |}{col 38}{res}{space 2} .9596302{col 50}{space 2} .0064689{col 61}{space 1}   -6.11{col 70}{space 3}0.000{col 78}{space 4}   .94703{col 91}{space 3} .9723981
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.596841{col 50}{space 2} .2458972{col 61}{space 1}    3.04{col 70}{space 3}0.002{col 78}{space 4} 1.180684{col 91}{space 3}  2.15968
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 2.007142{col 50}{space 2} .3321148{col 61}{space 1}    4.21{col 70}{space 3}0.000{col 78}{space 4} 1.451034{col 91}{space 3} 2.776378
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2}   2.7076{col 50}{space 2} .6855752{col 61}{space 1}    3.93{col 70}{space 3}0.000{col 78}{space 4} 1.648057{col 91}{space 3} 4.448326
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .7300879{col 50}{space 2} .1350894{col 61}{space 1}   -1.70{col 70}{space 3}0.089{col 78}{space 4}  .507942{col 91}{space 3} 1.049388
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .7059871{col 50}{space 2} .2381121{col 61}{space 1}   -1.03{col 70}{space 3}0.302{col 78}{space 4} .3644138{col 91}{space 3} 1.367725
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.008599{col 50}{space 2} .2977527{col 61}{space 1}    0.03{col 70}{space 3}0.977{col 78}{space 4} .5653712{col 91}{space 3} 1.799301
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.099827{col 50}{space 2} .5777874{col 61}{space 1}    0.18{col 70}{space 3}0.856{col 78}{space 4} .3926232{col 91}{space 3} 3.080868
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .4807767{col 50}{space 2} .1731931{col 61}{space 1}   -2.03{col 70}{space 3}0.042{col 78}{space 4}  .237241{col 91}{space 3} .9743098
{txt}{space 34}3  {c |}{col 38}{res}{space 2}  .385858{col 50}{space 2} .1234435{col 61}{space 1}   -2.98{col 70}{space 3}0.003{col 78}{space 4} .2060653{col 91}{space 3} .7225205
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .5935098{col 50}{space 2} .1669044{col 61}{space 1}   -1.86{col 70}{space 3}0.064{col 78}{space 4} .3419501{col 91}{space 3} 1.030133
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .7380867{col 50}{space 2} .2004006{col 61}{space 1}   -1.12{col 70}{space 3}0.263{col 78}{space 4}  .433415{col 91}{space 3} 1.256929
{txt}{space 36} {c |}
{space 23}std_RTI_Autor {c |}{col 38}{res}{space 2}   .69445{col 50}{space 2} .1119052{col 61}{space 1}   -2.26{col 70}{space 3}0.024{col 78}{space 4} .5063172{col 91}{space 3} .9524873
{txt}{space 9}std_offshorable_Blinder2007 {c |}{col 38}{res}{space 2} 1.147525{col 50}{space 2} .0883329{col 61}{space 1}    1.79{col 70}{space 3}0.074{col 78}{space 4} .9867647{col 91}{space 3} 1.334476
{txt}{space 31}_cons {c |}{col 38}{res}{space 2} .2067569{col 50}{space 2} .0924985{col 61}{space 1}   -3.52{col 70}{space 3}0.000{col 78}{space 4} .0860007{col 91}{space 3} .4970705
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. ** Model 3a
. svy: logit AI_directionworse aiexposure_Webb2020 if infullmodel_TableA2 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       994
{txt}{col 1}Number of PSUs{col 20}= {res}      994{txt}{col 49}Population size{col 67}={res} 1,027.2521
{txt}{col 49}Design df{col 67}= {res}       993
{txt}{col 49}F({res}   1{txt},{res}    993{txt}){col 67}= {res}      8.99
{txt}{col 49}Prob > F{col 67}= {res}    0.0028

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}  Linearized
{col 1}  AI_directionworse{col 21}{c |} Odds Ratio{col 33}   Std. Err.{col 45}      t{col 53}   P>|t|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
aiexposure_Webb2020 {c |}{col 21}{res}{space 2} .8087662{col 33}{space 2} .0572536{col 44}{space 1}   -3.00{col 53}{space 3}0.003{col 61}{space 4} .7038689{col 74}{space 3} .9292963
{txt}{space 14}_cons {c |}{col 21}{res}{space 2} 2.787322{col 33}{space 2} .2248441{col 44}{space 1}   12.71{col 53}{space 3}0.000{col 61}{space 4} 2.379248{col 74}{space 3} 3.265386
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 3b
. svy: logit AI_directionworse aiexposure_Webb2020 $controls_demographics if infullmodel_TableA2 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       994
{txt}{col 1}Number of PSUs{col 20}= {res}      994{txt}{col 49}Population size{col 67}={res} 1,027.2521
{txt}{col 49}Design df{col 67}= {res}       993
{txt}{col 49}F({res}  13{txt},{res}    981{txt}){col 67}= {res}      5.49
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                   AI_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}aiexposure_Webb2020 {c |}{col 38}{res}{space 2} .9269398{col 50}{space 2} .0741864{col 61}{space 1}   -0.95{col 70}{space 3}0.343{col 78}{space 4} .7922159{col 91}{space 3} 1.084575
{txt}{space 33}age {c |}{col 38}{res}{space 2} 1.033532{col 50}{space 2} .0078284{col 61}{space 1}    4.35{col 70}{space 3}0.000{col 78}{space 4} 1.018283{col 91}{space 3} 1.049009
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2}  .736935{col 50}{space 2} .1262633{col 61}{space 1}   -1.78{col 70}{space 3}0.075{col 78}{space 4} .5265141{col 91}{space 3}  1.03145
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}   .65215{col 50}{space 2}  .120281{col 61}{space 1}   -2.32{col 70}{space 3}0.021{col 78}{space 4} .4541113{col 91}{space 3} .9365538
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} .3112885{col 50}{space 2} .0865016{col 61}{space 1}   -4.20{col 70}{space 3}0.000{col 78}{space 4} .1804433{col 91}{space 3}  .537014
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2}  1.27054{col 50}{space 2} .2588943{col 61}{space 1}    1.18{col 70}{space 3}0.240{col 78}{space 4} .8517847{col 91}{space 3} 1.895164
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.339971{col 50}{space 2}  .499358{col 61}{space 1}    0.79{col 70}{space 3}0.432{col 78}{space 4} .6449068{col 91}{space 3} 2.784159
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9082415{col 50}{space 2} .3153444{col 61}{space 1}   -0.28{col 70}{space 3}0.782{col 78}{space 4} .4595162{col 91}{space 3} 1.795155
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .7266268{col 50}{space 2} .4230877{col 61}{space 1}   -0.55{col 70}{space 3}0.584{col 78}{space 4} .2317842{col 91}{space 3} 2.277924
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.955823{col 50}{space 2} .8002062{col 61}{space 1}    1.64{col 70}{space 3}0.101{col 78}{space 4} .8762817{col 91}{space 3} 4.365314
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 2.234599{col 50}{space 2} .8028263{col 61}{space 1}    2.24{col 70}{space 3}0.025{col 78}{space 4} 1.104115{col 91}{space 3} 4.522564
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.593446{col 50}{space 2} .5196849{col 61}{space 1}    1.43{col 70}{space 3}0.153{col 78}{space 4} .8402116{col 91}{space 3} 3.021943
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.271513{col 50}{space 2} .4014547{col 61}{space 1}    0.76{col 70}{space 3}0.447{col 78}{space 4} .6842948{col 91}{space 3} 2.362646
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 1.773457{col 50}{space 2} .8072551{col 61}{space 1}    1.26{col 70}{space 3}0.208{col 78}{space 4} .7259247{col 91}{space 3}  4.33261
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 3c
. svy: logit AI_directionworse aiexposure_Webb2020 $controls_demographics $controls_occupindices if infullmodel_TableA2 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       994
{txt}{col 1}Number of PSUs{col 20}= {res}      994{txt}{col 49}Population size{col 67}={res} 1,027.2521
{txt}{col 49}Design df{col 67}= {res}       993
{txt}{col 49}F({res}  15{txt},{res}    979{txt}){col 67}= {res}      5.10
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                   AI_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}aiexposure_Webb2020 {c |}{col 38}{res}{space 2}  .922145{col 50}{space 2} .0782931{col 61}{space 1}   -0.95{col 70}{space 3}0.340{col 78}{space 4} .7806229{col 91}{space 3} 1.089324
{txt}{space 33}age {c |}{col 38}{res}{space 2} 1.034753{col 50}{space 2}  .008076{col 61}{space 1}    4.38{col 70}{space 3}0.000{col 78}{space 4} 1.019026{col 91}{space 3} 1.050723
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} .7475256{col 50}{space 2}  .128608{col 61}{space 1}   -1.69{col 70}{space 3}0.091{col 78}{space 4} .5333378{col 91}{space 3} 1.047731
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7148915{col 50}{space 2} .1362044{col 61}{space 1}   -1.76{col 70}{space 3}0.078{col 78}{space 4} .4918894{col 91}{space 3} 1.038994
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2}  .338841{col 50}{space 2} .0948984{col 61}{space 1}   -3.86{col 70}{space 3}0.000{col 78}{space 4} .1955739{col 91}{space 3} .5870579
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.410751{col 50}{space 2} .3020858{col 61}{space 1}    1.61{col 70}{space 3}0.108{col 78}{space 4} .9267424{col 91}{space 3} 2.147542
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.318348{col 50}{space 2} .4801924{col 61}{space 1}    0.76{col 70}{space 3}0.448{col 78}{space 4}  .645077{col 91}{space 3} 2.694315
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.039806{col 50}{space 2} .3588316{col 61}{space 1}    0.11{col 70}{space 3}0.910{col 78}{space 4} .5282607{col 91}{space 3} 2.046708
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .7037279{col 50}{space 2} .4042722{col 61}{space 1}   -0.61{col 70}{space 3}0.541{col 78}{space 4} .2279378{col 91}{space 3} 2.172667
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.861686{col 50}{space 2} .7532658{col 61}{space 1}    1.54{col 70}{space 3}0.125{col 78}{space 4} .8415453{col 91}{space 3} 4.118466
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 2.182655{col 50}{space 2} .7792306{col 61}{space 1}    2.19{col 70}{space 3}0.029{col 78}{space 4} 1.083245{col 91}{space 3} 4.397885
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.661564{col 50}{space 2} .5406998{col 61}{space 1}    1.56{col 70}{space 3}0.119{col 78}{space 4} .8773735{col 91}{space 3}  3.14666
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2}  1.37272{col 50}{space 2} .4262215{col 61}{space 1}    1.02{col 70}{space 3}0.308{col 78}{space 4} .7463907{col 91}{space 3} 2.524629
{txt}{space 36} {c |}
{space 23}std_RTI_Autor {c |}{col 38}{res}{space 2} 1.714744{col 50}{space 2} .3227954{col 61}{space 1}    2.86{col 70}{space 3}0.004{col 78}{space 4} 1.185134{col 91}{space 3} 2.481024
{txt}{space 9}std_offshorable_Blinder2007 {c |}{col 38}{res}{space 2} 1.000581{col 50}{space 2} .0882787{col 61}{space 1}    0.01{col 70}{space 3}0.995{col 78}{space 4} .8415136{col 91}{space 3} 1.189716
{txt}{space 31}_cons {c |}{col 38}{res}{space 2} 1.432315{col 50}{space 2} .6757419{col 61}{space 1}    0.76{col 70}{space 3}0.447{col 78}{space 4} .5675004{col 91}{space 3} 3.615023
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. 
. 
. ************************************************************************************************************
. 
. * APPENDIX B: Full Models from Table 1
. 
. * TABLE B1: Distribution of Subjective Exposure to AI by Objective Occupational Exposure (Raw Data) *
.         
. * Set Controls and Sample Size  
. global controls_occupindices std_RTI_Autor std_offshorable_Blinder2007 
{txt}
{com}. global controls_demographics age ib1.female_dummy i.degree_dummy ib2.employmentstatus5 i.employsector5 i.equivincomeHHquintile_HBAI_imp         
{txt}
{com}.  
. svy: logit AI_worsen aiexposure_felten2021 $controls_demographics  $controls_occupindices, or
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}  15{txt},{res}   3947{txt}){col 67}= {res}      6.83
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2}  1.38664{col 50}{space 2} .0913931{col 61}{space 1}    4.96{col 70}{space 3}0.000{col 78}{space 4} 1.218552{col 91}{space 3} 1.577914
{txt}{space 33}age {c |}{col 38}{res}{space 2} .9931619{col 50}{space 2} .0036225{col 61}{space 1}   -1.88{col 70}{space 3}0.060{col 78}{space 4} .9860851{col 91}{space 3} 1.000289
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.123461{col 50}{space 2} .0981423{col 61}{space 1}    1.33{col 70}{space 3}0.183{col 78}{space 4} .9466223{col 91}{space 3} 1.333335
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.284636{col 50}{space 2} .1159889{col 61}{space 1}    2.77{col 70}{space 3}0.006{col 78}{space 4} 1.076223{col 91}{space 3} 1.533409
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} .7809298{col 50}{space 2} .0821598{col 61}{space 1}   -2.35{col 70}{space 3}0.019{col 78}{space 4} .6353774{col 91}{space 3} .9598255
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.041029{col 50}{space 2} .1012722{col 61}{space 1}    0.41{col 70}{space 3}0.679{col 78}{space 4} .8602643{col 91}{space 3} 1.259777
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9613448{col 50}{space 2} .1716262{col 61}{space 1}   -0.22{col 70}{space 3}0.825{col 78}{space 4} .6774388{col 91}{space 3} 1.364232
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.097393{col 50}{space 2} .1593472{col 61}{space 1}    0.64{col 70}{space 3}0.522{col 78}{space 4} .8255157{col 91}{space 3}  1.45881
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8541451{col 50}{space 2} .2433443{col 61}{space 1}   -0.55{col 70}{space 3}0.580{col 78}{space 4} .4885983{col 91}{space 3} 1.493177
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.113198{col 50}{space 2} .1854265{col 61}{space 1}    0.64{col 70}{space 3}0.520{col 78}{space 4} .8030516{col 91}{space 3} 1.543125
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.026798{col 50}{space 2} .1661183{col 61}{space 1}    0.16{col 70}{space 3}0.870{col 78}{space 4} .7477095{col 91}{space 3} 1.410057
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.033278{col 50}{space 2} .1629919{col 61}{space 1}    0.21{col 70}{space 3}0.836{col 78}{space 4} .7584124{col 91}{space 3} 1.407761
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.058673{col 50}{space 2}  .169062{col 61}{space 1}    0.36{col 70}{space 3}0.721{col 78}{space 4} .7740875{col 91}{space 3} 1.447884
{txt}{space 36} {c |}
{space 23}std_RTI_Autor {c |}{col 38}{res}{space 2} 1.454632{col 50}{space 2} .1212435{col 61}{space 1}    4.50{col 70}{space 3}0.000{col 78}{space 4} 1.235332{col 91}{space 3} 1.712862
{txt}{space 9}std_offshorable_Blinder2007 {c |}{col 38}{res}{space 2} 1.088253{col 50}{space 2} .0538328{col 61}{space 1}    1.71{col 70}{space 3}0.087{col 78}{space 4} .9876673{col 91}{space 3} 1.199083
{txt}{space 31}_cons {c |}{col 38}{res}{space 2} .3319043{col 50}{space 2} .0737426{col 61}{space 1}   -4.96{col 70}{space 3}0.000{col 78}{space 4} .2147014{col 91}{space 3} .5130868
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. *generate infullmodel_Table1 = e(sample) 
.          
.          
. ******* TABLE B1 ******
. 
. ** Model 1a
. svy: logit AI_worsen aiexposure_felten2021 if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}   1{txt},{res}   3961{txt}){col 67}= {res}     42.23
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}  Linearized
{col 1}            AI_worsen{col 23}{c |} Odds Ratio{col 35}   Std. Err.{col 47}      t{col 55}   P>|t|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
aiexposure_felten2021 {c |}{col 23}{res}{space 2} 1.418414{col 35}{space 2} .0762961{col 46}{space 1}    6.50{col 55}{space 3}0.000{col 63}{space 4} 1.276448{col 76}{space 3} 1.576169
{txt}{space 16}_cons {c |}{col 23}{res}{space 2} .2546991{col 35}{space 2} .0140968{col 46}{space 1}  -24.71{col 55}{space 3}0.000{col 63}{space 4}  .228508{col 76}{space 3} .2838921
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 1b
. svy: logit AI_worsen aiexposure_felten2021 $controls_demographics if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}  13{txt},{res}   3949{txt}){col 67}= {res}      4.83
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2}  1.41093{col 50}{space 2} .0823244{col 61}{space 1}    5.90{col 70}{space 3}0.000{col 78}{space 4} 1.258417{col 91}{space 3} 1.581926
{txt}{space 33}age {c |}{col 38}{res}{space 2} .9928402{col 50}{space 2} .0035926{col 61}{space 1}   -1.99{col 70}{space 3}0.047{col 78}{space 4} .9858215{col 91}{space 3} .9999088
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.134889{col 50}{space 2} .0979687{col 61}{space 1}    1.47{col 70}{space 3}0.143{col 78}{space 4} .9581894{col 91}{space 3} 1.344174
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.214095{col 50}{space 2} .1085179{col 61}{space 1}    2.17{col 70}{space 3}0.030{col 78}{space 4} 1.018937{col 91}{space 3} 1.446631
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2}  .763106{col 50}{space 2} .0799534{col 61}{space 1}   -2.58{col 70}{space 3}0.010{col 78}{space 4} .6214041{col 91}{space 3} .9371209
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9218484{col 50}{space 2} .0871412{col 61}{space 1}   -0.86{col 70}{space 3}0.389{col 78}{space 4} .7658997{col 91}{space 3} 1.109551
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .8932466{col 50}{space 2} .1594037{col 61}{space 1}   -0.63{col 70}{space 3}0.527{col 78}{space 4} .6295415{col 91}{space 3} 1.267414
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2}  .971413{col 50}{space 2} .1406076{col 61}{space 1}   -0.20{col 70}{space 3}0.841{col 78}{space 4} .7314064{col 91}{space 3} 1.290176
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8249163{col 50}{space 2} .2318424{col 61}{space 1}   -0.68{col 70}{space 3}0.493{col 78}{space 4} .4754522{col 91}{space 3} 1.431241
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.129994{col 50}{space 2} .1875537{col 61}{space 1}    0.74{col 70}{space 3}0.462{col 78}{space 4}  .816117{col 91}{space 3} 1.564587
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.008098{col 50}{space 2} .1622075{col 61}{space 1}    0.05{col 70}{space 3}0.960{col 78}{space 4} .7353577{col 91}{space 3} 1.381996
{txt}{space 34}4  {c |}{col 38}{res}{space 2}  1.00438{col 50}{space 2} .1570008{col 61}{space 1}    0.03{col 70}{space 3}0.978{col 78}{space 4} .7392659{col 91}{space 3} 1.364568
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .9993104{col 50}{space 2} .1575852{col 61}{space 1}   -0.00{col 70}{space 3}0.997{col 78}{space 4} .7335505{col 91}{space 3} 1.361353
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2}  .373499{col 50}{space 2}  .082692{col 61}{space 1}   -4.45{col 70}{space 3}0.000{col 78}{space 4} .2419788{col 91}{space 3}  .576503
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 1c
. svy: logit AI_worsen aiexposure_felten2021 $controls_demographics $controls_occupindices if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}  15{txt},{res}   3947{txt}){col 67}= {res}      6.83
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2}  1.38664{col 50}{space 2} .0913931{col 61}{space 1}    4.96{col 70}{space 3}0.000{col 78}{space 4} 1.218552{col 91}{space 3} 1.577914
{txt}{space 33}age {c |}{col 38}{res}{space 2} .9931619{col 50}{space 2} .0036225{col 61}{space 1}   -1.88{col 70}{space 3}0.060{col 78}{space 4} .9860851{col 91}{space 3} 1.000289
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.123461{col 50}{space 2} .0981423{col 61}{space 1}    1.33{col 70}{space 3}0.183{col 78}{space 4} .9466223{col 91}{space 3} 1.333335
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.284636{col 50}{space 2} .1159889{col 61}{space 1}    2.77{col 70}{space 3}0.006{col 78}{space 4} 1.076223{col 91}{space 3} 1.533409
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} .7809298{col 50}{space 2} .0821598{col 61}{space 1}   -2.35{col 70}{space 3}0.019{col 78}{space 4} .6353774{col 91}{space 3} .9598255
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.041029{col 50}{space 2} .1012722{col 61}{space 1}    0.41{col 70}{space 3}0.679{col 78}{space 4} .8602643{col 91}{space 3} 1.259777
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9613448{col 50}{space 2} .1716262{col 61}{space 1}   -0.22{col 70}{space 3}0.825{col 78}{space 4} .6774388{col 91}{space 3} 1.364232
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.097393{col 50}{space 2} .1593472{col 61}{space 1}    0.64{col 70}{space 3}0.522{col 78}{space 4} .8255157{col 91}{space 3}  1.45881
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8541451{col 50}{space 2} .2433443{col 61}{space 1}   -0.55{col 70}{space 3}0.580{col 78}{space 4} .4885983{col 91}{space 3} 1.493177
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.113198{col 50}{space 2} .1854265{col 61}{space 1}    0.64{col 70}{space 3}0.520{col 78}{space 4} .8030516{col 91}{space 3} 1.543125
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.026798{col 50}{space 2} .1661183{col 61}{space 1}    0.16{col 70}{space 3}0.870{col 78}{space 4} .7477095{col 91}{space 3} 1.410057
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.033278{col 50}{space 2} .1629919{col 61}{space 1}    0.21{col 70}{space 3}0.836{col 78}{space 4} .7584124{col 91}{space 3} 1.407761
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.058673{col 50}{space 2}  .169062{col 61}{space 1}    0.36{col 70}{space 3}0.721{col 78}{space 4} .7740875{col 91}{space 3} 1.447884
{txt}{space 36} {c |}
{space 23}std_RTI_Autor {c |}{col 38}{res}{space 2} 1.454632{col 50}{space 2} .1212435{col 61}{space 1}    4.50{col 70}{space 3}0.000{col 78}{space 4} 1.235332{col 91}{space 3} 1.712862
{txt}{space 9}std_offshorable_Blinder2007 {c |}{col 38}{res}{space 2} 1.088253{col 50}{space 2} .0538328{col 61}{space 1}    1.71{col 70}{space 3}0.087{col 78}{space 4} .9876673{col 91}{space 3} 1.199083
{txt}{space 31}_cons {c |}{col 38}{res}{space 2} .3319043{col 50}{space 2} .0737426{col 61}{space 1}   -4.96{col 70}{space 3}0.000{col 78}{space 4} .2147014{col 91}{space 3} .5130868
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. ** Model 2a
. svy: logit AI_improve aiexposure_felten2021 if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}   1{txt},{res}   3961{txt}){col 67}= {res}     37.96
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}  Linearized
{col 1}           AI_improve{col 23}{c |} Odds Ratio{col 35}   Std. Err.{col 47}      t{col 55}   P>|t|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
aiexposure_felten2021 {c |}{col 23}{res}{space 2} 1.842481{col 35}{space 2} .1827491{col 46}{space 1}    6.16{col 55}{space 3}0.000{col 63}{space 4} 1.516874{col 76}{space 3} 2.237981
{txt}{space 16}_cons {c |}{col 23}{res}{space 2} .0691515{col 35}{space 2} .0072462{col 46}{space 1}  -25.49{col 55}{space 3}0.000{col 63}{space 4} .0563091{col 76}{space 3} .0849228
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 2b
. svy: logit AI_improve aiexposure_felten2021 $controls_demographics if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}  13{txt},{res}   3949{txt}){col 67}= {res}     12.87
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                          AI_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.566415{col 50}{space 2} .1600653{col 61}{space 1}    4.39{col 70}{space 3}0.000{col 78}{space 4} 1.282035{col 91}{space 3} 1.913878
{txt}{space 33}age {c |}{col 38}{res}{space 2} .9693991{col 50}{space 2} .0055578{col 61}{space 1}   -5.42{col 70}{space 3}0.000{col 78}{space 4} .9585636{col 91}{space 3}  .980357
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.717065{col 50}{space 2} .2192722{col 61}{space 1}    4.23{col 70}{space 3}0.000{col 78}{space 4} 1.336761{col 91}{space 3} 2.205565
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  1.75945{col 50}{space 2} .2423788{col 61}{space 1}    4.10{col 70}{space 3}0.000{col 78}{space 4} 1.343015{col 91}{space 3}  2.30501
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} 2.128802{col 50}{space 2} .4370058{col 61}{space 1}    3.68{col 70}{space 3}0.000{col 78}{space 4} 1.423459{col 91}{space 3} 3.183651
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .7492511{col 50}{space 2}  .107168{col 61}{space 1}   -2.02{col 70}{space 3}0.044{col 78}{space 4} .5660304{col 91}{space 3} .9917792
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .7118171{col 50}{space 2} .1990685{col 61}{space 1}   -1.22{col 70}{space 3}0.224{col 78}{space 4} .4113832{col 91}{space 3} 1.231658
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.246509{col 50}{space 2} .2828993{col 61}{space 1}    0.97{col 70}{space 3}0.332{col 78}{space 4} .7988279{col 91}{space 3}  1.94508
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.206108{col 50}{space 2} .6107226{col 61}{space 1}    0.37{col 70}{space 3}0.711{col 78}{space 4} .4469319{col 91}{space 3} 3.254849
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5007042{col 50}{space 2} .1490934{col 61}{space 1}   -2.32{col 70}{space 3}0.020{col 78}{space 4} .2792825{col 91}{space 3} .8976741
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .4284354{col 50}{space 2} .1165606{col 61}{space 1}   -3.12{col 70}{space 3}0.002{col 78}{space 4} .2513251{col 91}{space 3} .7303565
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .5153574{col 50}{space 2} .1254572{col 61}{space 1}   -2.72{col 70}{space 3}0.006{col 78}{space 4} .3197651{col 91}{space 3} .8305889
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .6979932{col 50}{space 2} .1635321{col 61}{space 1}   -1.53{col 70}{space 3}0.125{col 78}{space 4} .4409234{col 91}{space 3} 1.104942
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .1467656{col 50}{space 2} .0550539{col 61}{space 1}   -5.12{col 70}{space 3}0.000{col 78}{space 4} .0703442{col 91}{space 3} .3062105
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 2c
. svy: logit AI_improve aiexposure_felten2021 $controls_demographics $controls_occupindices if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}  15{txt},{res}   3947{txt}){col 67}= {res}     11.15
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                          AI_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.468642{col 50}{space 2} .1685801{col 61}{space 1}    3.35{col 70}{space 3}0.001{col 78}{space 4}  1.17268{col 91}{space 3} 1.839298
{txt}{space 33}age {c |}{col 38}{res}{space 2} .9691886{col 50}{space 2} .0055792{col 61}{space 1}   -5.44{col 70}{space 3}0.000{col 78}{space 4} .9583117{col 91}{space 3}  .980189
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.693684{col 50}{space 2} .2168789{col 61}{space 1}    4.11{col 70}{space 3}0.000{col 78}{space 4} 1.317654{col 91}{space 3} 2.177025
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.725848{col 50}{space 2} .2409989{col 61}{space 1}    3.91{col 70}{space 3}0.000{col 78}{space 4} 1.312513{col 91}{space 3} 2.269349
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} 2.074592{col 50}{space 2} .4252539{col 61}{space 1}    3.56{col 70}{space 3}0.000{col 78}{space 4} 1.388029{col 91}{space 3} 3.100752
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .7535466{col 50}{space 2}  .113038{col 61}{space 1}   -1.89{col 70}{space 3}0.059{col 78}{space 4} .5615443{col 91}{space 3} 1.011198
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .7131232{col 50}{space 2} .2009502{col 61}{space 1}   -1.20{col 70}{space 3}0.230{col 78}{space 4} .4104234{col 91}{space 3} 1.239073
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.201297{col 50}{space 2} .2748975{col 61}{space 1}    0.80{col 70}{space 3}0.423{col 78}{space 4} .7670205{col 91}{space 3} 1.881454
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.226089{col 50}{space 2} .6238686{col 61}{space 1}    0.40{col 70}{space 3}0.689{col 78}{space 4} .4521417{col 91}{space 3} 3.324832
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5010162{col 50}{space 2}  .148981{col 61}{space 1}   -2.32{col 70}{space 3}0.020{col 78}{space 4} .2796811{col 91}{space 3} .8975123
{txt}{space 34}3  {c |}{col 38}{res}{space 2}  .426497{col 50}{space 2}  .115992{col 61}{space 1}   -3.13{col 70}{space 3}0.002{col 78}{space 4} .2502353{col 91}{space 3} .7269145
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .5101341{col 50}{space 2} .1247094{col 61}{space 1}   -2.75{col 70}{space 3}0.006{col 78}{space 4} .3158877{col 91}{space 3}  .823827
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .6756307{col 50}{space 2} .1586321{col 61}{space 1}   -1.67{col 70}{space 3}0.095{col 78}{space 4}  .426377{col 91}{space 3} 1.070595
{txt}{space 36} {c |}
{space 23}std_RTI_Autor {c |}{col 38}{res}{space 2} .8142681{col 50}{space 2} .1151217{col 61}{space 1}   -1.45{col 70}{space 3}0.146{col 78}{space 4} .6171451{col 91}{space 3} 1.074354
{txt}{space 9}std_offshorable_Blinder2007 {c |}{col 38}{res}{space 2}   1.0862{col 50}{space 2} .0788164{col 61}{space 1}    1.14{col 70}{space 3}0.255{col 78}{space 4} .9421639{col 91}{space 3} 1.252256
{txt}{space 31}_cons {c |}{col 38}{res}{space 2} .1577769{col 50}{space 2} .0599877{col 61}{space 1}   -4.86{col 70}{space 3}0.000{col 78}{space 4} .0748709{col 91}{space 3} .3324864
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. ** Model 3a
. svy: logit AI_directionworse aiexposure_felten2021 if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}   1{txt},{res}   1321{txt}){col 67}= {res}      7.20
{txt}{col 49}Prob > F{col 67}= {res}    0.0074

{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}  Linearized
{col 1}    AI_directionworse{col 23}{c |} Odds Ratio{col 35}   Std. Err.{col 47}      t{col 55}   P>|t|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
aiexposure_felten2021 {c |}{col 23}{res}{space 2} .7601341{col 35}{space 2} .0776837{col 46}{space 1}   -2.68{col 55}{space 3}0.007{col 63}{space 4} .6220423{col 76}{space 3} .9288821
{txt}{space 16}_cons {c |}{col 23}{res}{space 2} 3.104447{col 35}{space 2} .3424206{col 46}{space 1}   10.27{col 55}{space 3}0.000{col 63}{space 4} 2.500406{col 76}{space 3}  3.85441
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 3b
. svy: logit AI_directionworse aiexposure_felten2021 $controls_demographics if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}  13{txt},{res}   1309{txt}){col 67}= {res}      5.62
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                   AI_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .8435581{col 50}{space 2} .0899951{col 61}{space 1}   -1.59{col 70}{space 3}0.111{col 78}{space 4}   .68426{col 91}{space 3} 1.039941
{txt}{space 33}age {c |}{col 38}{res}{space 2} 1.021888{col 50}{space 2} .0063162{col 61}{space 1}    3.50{col 70}{space 3}0.000{col 78}{space 4} 1.009572{col 91}{space 3} 1.034354
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} .6928938{col 50}{space 2} .0993854{col 61}{space 1}   -2.56{col 70}{space 3}0.011{col 78}{space 4} .5229527{col 91}{space 3} .9180598
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  .698706{col 50}{space 2} .1064464{col 61}{space 1}   -2.35{col 70}{space 3}0.019{col 78}{space 4} .5181993{col 91}{space 3} .9420896
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} .4152781{col 50}{space 2} .0931261{col 61}{space 1}   -3.92{col 70}{space 3}0.000{col 78}{space 4} .2674743{col 91}{space 3} .6447569
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.215059{col 50}{space 2} .1958642{col 61}{space 1}    1.21{col 70}{space 3}0.227{col 78}{space 4} .8856464{col 91}{space 3} 1.666996
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.260586{col 50}{space 2} .4078532{col 61}{space 1}    0.72{col 70}{space 3}0.474{col 78}{space 4} .6682209{col 91}{space 3} 2.378072
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9058492{col 50}{space 2} .2407181{col 61}{space 1}   -0.37{col 70}{space 3}0.710{col 78}{space 4}  .537839{col 91}{space 3} 1.525666
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .7311951{col 50}{space 2} .3968104{col 61}{space 1}   -0.58{col 70}{space 3}0.564{col 78}{space 4} .2521583{col 91}{space 3}  2.12028
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}  1.92555{col 50}{space 2}  .654354{col 61}{space 1}    1.93{col 70}{space 3}0.054{col 78}{space 4} .9886171{col 91}{space 3} 3.750433
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 2.183719{col 50}{space 2} .6699048{col 61}{space 1}    2.55{col 70}{space 3}0.011{col 78}{space 4} 1.196278{col 91}{space 3} 3.986221
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.748867{col 50}{space 2} .4897441{col 61}{space 1}    2.00{col 70}{space 3}0.046{col 78}{space 4} 1.009652{col 91}{space 3} 3.029296
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.319614{col 50}{space 2} .3588896{col 61}{space 1}    1.02{col 70}{space 3}0.308{col 78}{space 4} .7739932{col 91}{space 3} 2.249867
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 2.294478{col 50}{space 2}  .895971{col 61}{space 1}    2.13{col 70}{space 3}0.034{col 78}{space 4}  1.06658{col 91}{space 3} 4.935993
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 3c
. svy: logit AI_directionworse aiexposure_felten2021 $controls_demographics $controls_occupindices if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}  15{txt},{res}   1307{txt}){col 67}= {res}      5.13
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                   AI_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .8918794{col 50}{space 2} .1080793{col 61}{space 1}   -0.94{col 70}{space 3}0.345{col 78}{space 4}  .703172{col 91}{space 3} 1.131229
{txt}{space 33}age {c |}{col 38}{res}{space 2} 1.022598{col 50}{space 2} .0064167{col 61}{space 1}    3.56{col 70}{space 3}0.000{col 78}{space 4} 1.010087{col 91}{space 3} 1.035264
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} .7092891{col 50}{space 2}  .102144{col 61}{space 1}   -2.39{col 70}{space 3}0.017{col 78}{space 4} .5347246{col 91}{space 3} .9408413
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7334765{col 50}{space 2} .1137611{col 61}{space 1}   -2.00{col 70}{space 3}0.046{col 78}{space 4} .5410596{col 91}{space 3} .9943226
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} .4406998{col 50}{space 2} .0995899{col 61}{space 1}   -3.63{col 70}{space 3}0.000{col 78}{space 4} .2828856{col 91}{space 3} .6865542
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.299859{col 50}{space 2}  .221364{col 61}{space 1}    1.54{col 70}{space 3}0.124{col 78}{space 4} .9306895{col 91}{space 3} 1.815463
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}  1.25828{col 50}{space 2} .4031593{col 61}{space 1}    0.72{col 70}{space 3}0.473{col 78}{space 4} .6711163{col 91}{space 3} 2.359156
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.005385{col 50}{space 2} .2675771{col 61}{space 1}    0.02{col 70}{space 3}0.984{col 78}{space 4} .5964617{col 91}{space 3} 1.694659
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .7269527{col 50}{space 2} .3970539{col 61}{space 1}   -0.58{col 70}{space 3}0.559{col 78}{space 4} .2489789{col 91}{space 3} 2.122511
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.865272{col 50}{space 2} .6314466{col 61}{space 1}    1.84{col 70}{space 3}0.066{col 78}{space 4} .9601136{col 91}{space 3} 3.623781
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 2.158848{col 50}{space 2} .6611652{col 61}{space 1}    2.51{col 70}{space 3}0.012{col 78}{space 4} 1.183847{col 91}{space 3} 3.936849
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.787434{col 50}{space 2} .5019315{col 61}{space 1}    2.07{col 70}{space 3}0.039{col 78}{space 4} 1.030348{col 91}{space 3} 3.100817
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.380914{col 50}{space 2} .3723912{col 61}{space 1}    1.20{col 70}{space 3}0.232{col 78}{space 4} .8136031{col 91}{space 3} 2.343801
{txt}{space 36} {c |}
{space 23}std_RTI_Autor {c |}{col 38}{res}{space 2} 1.521521{col 50}{space 2} .2546298{col 61}{space 1}    2.51{col 70}{space 3}0.012{col 78}{space 4} 1.095714{col 91}{space 3} 2.112803
{txt}{space 9}std_offshorable_Blinder2007 {c |}{col 38}{res}{space 2}  .976218{col 50}{space 2} .0795256{col 61}{space 1}   -0.30{col 70}{space 3}0.768{col 78}{space 4} .8320355{col 91}{space 3} 1.145386
{txt}{space 31}_cons {c |}{col 38}{res}{space 2} 1.939541{col 50}{space 2} .7832764{col 61}{space 1}    1.64{col 70}{space 3}0.101{col 78}{space 4} .8782726{col 91}{space 3} 4.283204
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. 
. ******* TABLE B2 ******
. 
. ** Model 1a
. svy: logit AI_worsen i.aiexposure_pizzinelli2023 if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}   2{txt},{res}   3960{txt}){col 67}= {res}     26.05
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 33}{c |}{col 45}  Linearized
{col 1}                      AI_worsen{col 33}{c |} Odds Ratio{col 45}   Std. Err.{col 57}      t{col 65}   P>|t|{col 73}     [95% Con{col 86}f. Interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}aiexposure_pizzinelli2023 {c |}
{space 1}High Exposure, Low Compliment  {c |}{col 33}{res}{space 2} 2.253628{col 45}{space 2} .2685664{col 56}{space 1}    6.82{col 65}{space 3}0.000{col 73}{space 4} 1.784075{col 86}{space 3} 2.846765
{txt}High Exposure, High Compliment  {c |}{col 33}{res}{space 2} 1.541842{col 45}{space 2} .2002989{col 56}{space 1}    3.33{col 65}{space 3}0.001{col 73}{space 4} 1.195164{col 86}{space 3} 1.989081
{txt}{space 31} {c |}
{space 26}_cons {c |}{col 33}{res}{space 2} .1812414{col 45}{space 2} .0192322{col 56}{space 1}  -16.10{col 65}{space 3}0.000{col 73}{space 4} .1471994{col 86}{space 3} .2231563
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 1b
. svy: logit AI_worsen i.aiexposure_pizzinelli2023 $controls_demographics if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}  14{txt},{res}   3948{txt}){col 67}= {res}      5.50
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 2.127966{col 50}{space 2} .2662908{col 61}{space 1}    6.03{col 70}{space 3}0.000{col 78}{space 4} 1.664999{col 91}{space 3} 2.719666
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.444545{col 50}{space 2} .2003068{col 61}{space 1}    2.65{col 70}{space 3}0.008{col 78}{space 4} 1.100687{col 91}{space 3} 1.895825
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2} .9933672{col 50}{space 2} .0035854{col 61}{space 1}   -1.84{col 70}{space 3}0.065{col 78}{space 4} .9863625{col 91}{space 3} 1.000422
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.120267{col 50}{space 2} .0973125{col 61}{space 1}    1.31{col 70}{space 3}0.191{col 78}{space 4} .9448416{col 91}{space 3} 1.328263
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.274102{col 50}{space 2} .1143964{col 61}{space 1}    2.70{col 70}{space 3}0.007{col 78}{space 4} 1.068452{col 91}{space 3} 1.519334
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} .8055607{col 50}{space 2} .0841885{col 61}{space 1}   -2.07{col 70}{space 3}0.039{col 78}{space 4} .6563156{col 91}{space 3} .9887439
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9406172{col 50}{space 2} .0894352{col 61}{space 1}   -0.64{col 70}{space 3}0.520{col 78}{space 4} .7806471{col 91}{space 3} 1.133368
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}  .934951{col 50}{space 2} .1678696{col 61}{space 1}   -0.37{col 70}{space 3}0.708{col 78}{space 4} .6575211{col 91}{space 3} 1.329438
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9959776{col 50}{space 2} .1445435{col 61}{space 1}   -0.03{col 70}{space 3}0.978{col 78}{space 4} .7493406{col 91}{space 3} 1.323793
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .7964022{col 50}{space 2} .2207405{col 61}{space 1}   -0.82{col 70}{space 3}0.412{col 78}{space 4} .4625205{col 91}{space 3} 1.371305
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.131899{col 50}{space 2} .1885877{col 61}{space 1}    0.74{col 70}{space 3}0.457{col 78}{space 4} .8164775{col 91}{space 3} 1.569175
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.043966{col 50}{space 2} .1683346{col 61}{space 1}    0.27{col 70}{space 3}0.790{col 78}{space 4} .7610135{col 91}{space 3} 1.432124
{txt}{space 34}4  {c |}{col 38}{res}{space 2}  1.07852{col 50}{space 2} .1697266{col 61}{space 1}    0.48{col 70}{space 3}0.631{col 78}{space 4} .7921978{col 91}{space 3} 1.468326
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.112506{col 50}{space 2} .1751573{col 61}{space 1}    0.68{col 70}{space 3}0.498{col 78}{space 4}  .817043{col 91}{space 3} 1.514816
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2396575{col 50}{space 2} .0578177{col 61}{space 1}   -5.92{col 70}{space 3}0.000{col 78}{space 4} .1493395{col 91}{space 3} .3845984
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 1c
. svy: logit AI_worsen i.aiexposure_pizzinelli2023 $controls_demographics $controls_occupindices if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}  16{txt},{res}   3946{txt}){col 67}= {res}      6.39
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.923251{col 50}{space 2} .2674089{col 61}{space 1}    4.70{col 70}{space 3}0.000{col 78}{space 4} 1.464362{col 91}{space 3} 2.525941
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.705886{col 50}{space 2} .2632752{col 61}{space 1}    3.46{col 70}{space 3}0.001{col 78}{space 4} 1.260494{col 91}{space 3} 2.308655
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2} .9933648{col 50}{space 2} .0036102{col 61}{space 1}   -1.83{col 70}{space 3}0.067{col 78}{space 4} .9863119{col 91}{space 3} 1.000468
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.116024{col 50}{space 2} .0979441{col 61}{space 1}    1.25{col 70}{space 3}0.211{col 78}{space 4} .9396106{col 91}{space 3}  1.32556
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.302961{col 50}{space 2} .1179495{col 61}{space 1}    2.92{col 70}{space 3}0.003{col 78}{space 4} 1.091072{col 91}{space 3} 1.555999
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} .8004222{col 50}{space 2} .0843795{col 61}{space 1}   -2.11{col 70}{space 3}0.035{col 78}{space 4} .6509672{col 91}{space 3} .9841904
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.036596{col 50}{space 2} .1007126{col 61}{space 1}    0.37{col 70}{space 3}0.711{col 78}{space 4} .8568089{col 91}{space 3} 1.254108
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9744011{col 50}{space 2} .1743981{col 61}{space 1}   -0.14{col 70}{space 3}0.885{col 78}{space 4} .6860303{col 91}{space 3} 1.383988
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.099719{col 50}{space 2} .1603478{col 61}{space 1}    0.65{col 70}{space 3}0.514{col 78}{space 4} .8262885{col 91}{space 3} 1.463631
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8298116{col 50}{space 2} .2328845{col 61}{space 1}   -0.66{col 70}{space 3}0.506{col 78}{space 4}  .478651{col 91}{space 3}   1.4386
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}  1.11893{col 50}{space 2} .1866425{col 61}{space 1}    0.67{col 70}{space 3}0.501{col 78}{space 4} .8068175{col 91}{space 3} 1.551781
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.049268{col 50}{space 2}   .16988{col 61}{space 1}    0.30{col 70}{space 3}0.766{col 78}{space 4} .7638918{col 91}{space 3} 1.441255
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.075045{col 50}{space 2} .1703975{col 61}{space 1}    0.46{col 70}{space 3}0.648{col 78}{space 4} .7878938{col 91}{space 3} 1.466849
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.118386{col 50}{space 2}  .178308{col 61}{space 1}    0.70{col 70}{space 3}0.483{col 78}{space 4} .8181642{col 91}{space 3} 1.528774
{txt}{space 36} {c |}
{space 23}std_RTI_Autor {c |}{col 38}{res}{space 2} 1.386005{col 50}{space 2} .1245028{col 61}{space 1}    3.63{col 70}{space 3}0.000{col 78}{space 4} 1.162195{col 91}{space 3} 1.652914
{txt}{space 9}std_offshorable_Blinder2007 {c |}{col 38}{res}{space 2}  1.11532{col 50}{space 2} .0534567{col 61}{space 1}    2.28{col 70}{space 3}0.023{col 78}{space 4} 1.015288{col 91}{space 3} 1.225207
{txt}{space 31}_cons {c |}{col 38}{res}{space 2} .2299905{col 50}{space 2} .0563192{col 61}{space 1}   -6.00{col 70}{space 3}0.000{col 78}{space 4} .1423007{col 91}{space 3} .3717172
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. ** Model 2a
. svy: logit AI_improve i.aiexposure_pizzinelli2023 if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}   2{txt},{res}   3960{txt}){col 67}= {res}     15.52
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 33}{c |}{col 45}  Linearized
{col 1}                     AI_improve{col 33}{c |} Odds Ratio{col 45}   Std. Err.{col 57}      t{col 65}   P>|t|{col 73}     [95% Con{col 86}f. Interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}aiexposure_pizzinelli2023 {c |}
{space 1}High Exposure, Low Compliment  {c |}{col 33}{res}{space 2} 2.531605{col 45}{space 2} .5272212{col 56}{space 1}    4.46{col 65}{space 3}0.000{col 73}{space 4} 1.682961{col 86}{space 3} 3.808182
{txt}High Exposure, High Compliment  {c |}{col 33}{res}{space 2}  3.25763{col 45}{space 2} .6905999{col 56}{space 1}    5.57{col 65}{space 3}0.000{col 73}{space 4} 2.149792{col 86}{space 3} 4.936363
{txt}{space 31} {c |}
{space 26}_cons {c |}{col 33}{res}{space 2}  .044145{col 45}{space 2} .0083744{col 56}{space 1}  -16.45{col 65}{space 3}0.000{col 73}{space 4} .0304339{col 86}{space 3} .0640332
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 2b
. svy: logit AI_improve i.aiexposure_pizzinelli2023 $controls_demographics if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}  14{txt},{res}   3948{txt}){col 67}= {res}     11.85
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                          AI_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 2.035855{col 50}{space 2} .4522144{col 61}{space 1}    3.20{col 70}{space 3}0.001{col 78}{space 4} 1.317091{col 91}{space 3} 3.146863
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2}  2.55617{col 50}{space 2} .5905945{col 61}{space 1}    4.06{col 70}{space 3}0.000{col 78}{space 4} 1.625033{col 91}{space 3} 4.020843
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2} .9689069{col 50}{space 2} .0055329{col 61}{space 1}   -5.53{col 70}{space 3}0.000{col 78}{space 4} .9581199{col 91}{space 3} .9798154
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.752125{col 50}{space 2} .2255391{col 61}{space 1}    4.36{col 70}{space 3}0.000{col 78}{space 4} 1.361327{col 91}{space 3} 2.255112
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.793201{col 50}{space 2} .2481294{col 61}{space 1}    4.22{col 70}{space 3}0.000{col 78}{space 4} 1.367131{col 91}{space 3} 2.352056
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} 2.172154{col 50}{space 2} .4432291{col 61}{space 1}    3.80{col 70}{space 3}0.000{col 78}{space 4}  1.45596{col 91}{space 3} 3.240648
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2}   .70643{col 50}{space 2}  .102319{col 61}{space 1}   -2.40{col 70}{space 3}0.016{col 78}{space 4} .5317944{col 91}{space 3} .9384141
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .6770116{col 50}{space 2}  .190249{col 61}{space 1}   -1.39{col 70}{space 3}0.165{col 78}{space 4} .3902334{col 91}{space 3}  1.17454
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2}  1.23869{col 50}{space 2} .2810971{col 61}{space 1}    0.94{col 70}{space 3}0.346{col 78}{space 4} .7938524{col 91}{space 3} 1.932794
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.100193{col 50}{space 2} .5629756{col 61}{space 1}    0.19{col 70}{space 3}0.852{col 78}{space 4} .4034321{col 91}{space 3} 3.000317
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5154177{col 50}{space 2} .1530353{col 61}{space 1}   -2.23{col 70}{space 3}0.026{col 78}{space 4} .2879701{col 91}{space 3} .9225103
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .4436094{col 50}{space 2}  .121602{col 61}{space 1}   -2.97{col 70}{space 3}0.003{col 78}{space 4} .2591783{col 91}{space 3} .7592817
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .5415207{col 50}{space 2} .1313897{col 61}{space 1}   -2.53{col 70}{space 3}0.012{col 78}{space 4} .3365302{col 91}{space 3} .8713768
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .7509072{col 50}{space 2} .1760488{col 61}{space 1}   -1.22{col 70}{space 3}0.222{col 78}{space 4} .4742012{col 91}{space 3} 1.189077
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .0973069{col 50}{space 2} .0394148{col 61}{space 1}   -5.75{col 70}{space 3}0.000{col 78}{space 4} .0439798{col 91}{space 3} .2152947
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 2c
. svy: logit AI_improve i.aiexposure_pizzinelli2023 $controls_demographics $controls_occupindices if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}  16{txt},{res}   3946{txt}){col 67}= {res}     10.68
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                          AI_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.675884{col 50}{space 2} .4164535{col 61}{space 1}    2.08{col 70}{space 3}0.038{col 78}{space 4} 1.029574{col 91}{space 3} 2.727911
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 2.191788{col 50}{space 2} .5537442{col 61}{space 1}    3.11{col 70}{space 3}0.002{col 78}{space 4} 1.335614{col 91}{space 3} 3.596798
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2}  .968715{col 50}{space 2} .0055566{col 61}{space 1}   -5.54{col 70}{space 3}0.000{col 78}{space 4} .9578819{col 91}{space 3} .9796705
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.699053{col 50}{space 2} .2197333{col 61}{space 1}    4.10{col 70}{space 3}0.000{col 78}{space 4}  1.31853{col 91}{space 3} 2.189394
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.749331{col 50}{space 2} .2450256{col 61}{space 1}    3.99{col 70}{space 3}0.000{col 78}{space 4} 1.329258{col 91}{space 3} 2.302156
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} 2.078264{col 50}{space 2} .4251374{col 61}{space 1}    3.58{col 70}{space 3}0.000{col 78}{space 4} 1.391626{col 91}{space 3} 3.103694
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .7511533{col 50}{space 2} .1144864{col 61}{space 1}   -1.88{col 70}{space 3}0.061{col 78}{space 4} .5571264{col 91}{space 3} 1.012753
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .6970752{col 50}{space 2}  .198283{col 61}{space 1}   -1.27{col 70}{space 3}0.205{col 78}{space 4} .3990997{col 91}{space 3} 1.217525
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.227127{col 50}{space 2} .2791133{col 61}{space 1}    0.90{col 70}{space 3}0.368{col 78}{space 4} .7856379{col 91}{space 3} 1.916712
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.163564{col 50}{space 2} .5956368{col 61}{space 1}    0.30{col 70}{space 3}0.767{col 78}{space 4} .4265017{col 91}{space 3} 3.174385
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5087973{col 50}{space 2} .1514218{col 61}{space 1}   -2.27{col 70}{space 3}0.023{col 78}{space 4} .2838858{col 91}{space 3} .9118975
{txt}{space 34}3  {c |}{col 38}{res}{space 2}  .438489{col 50}{space 2} .1205689{col 61}{space 1}   -3.00{col 70}{space 3}0.003{col 78}{space 4} .2557625{col 91}{space 3} .7517622
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .5279368{col 50}{space 2} .1288841{col 61}{space 1}   -2.62{col 70}{space 3}0.009{col 78}{space 4}  .327127{col 91}{space 3} .8520157
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .7110663{col 50}{space 2} .1668514{col 61}{space 1}   -1.45{col 70}{space 3}0.146{col 78}{space 4} .4488643{col 91}{space 3} 1.126433
{txt}{space 36} {c |}
{space 23}std_RTI_Autor {c |}{col 38}{res}{space 2} .8524696{col 50}{space 2}  .135536{col 61}{space 1}   -1.00{col 70}{space 3}0.315{col 78}{space 4} .6241703{col 91}{space 3} 1.164273
{txt}{space 9}std_offshorable_Blinder2007 {c |}{col 38}{res}{space 2} 1.168567{col 50}{space 2} .0850367{col 61}{space 1}    2.14{col 70}{space 3}0.032{col 78}{space 4} 1.013194{col 91}{space 3} 1.347766
{txt}{space 31}_cons {c |}{col 38}{res}{space 2} .1177988{col 50}{space 2} .0494146{col 61}{space 1}   -5.10{col 70}{space 3}0.000{col 78}{space 4} .0517568{col 91}{space 3} .2681108
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. ** Model 3a
. svy: logit AI_directionworse ib3.aiexposure_pizzinelli2023 if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}   2{txt},{res}   1320{txt}){col 67}= {res}      8.12
{txt}{col 49}Prob > F{col 67}= {res}    0.0003

{txt}{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 32}{c |}{col 44}  Linearized
{col 1}             AI_directionworse{col 32}{c |} Odds Ratio{col 44}   Std. Err.{col 56}      t{col 64}   P>|t|{col 72}     [95% Con{col 85}f. Interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}aiexposure_pizzinelli2023 {c |}
{space 17}Low Exposure  {c |}{col 32}{res}{space 2} 2.089068{col 44}{space 2}  .495564{col 55}{space 1}    3.11{col 64}{space 3}0.002{col 72}{space 4} 1.311739{col 85}{space 3} 3.327039
{txt}High Exposure, Low Compliment  {c |}{col 32}{res}{space 2} 1.660678{col 44}{space 2} .2404057{col 55}{space 1}    3.50{col 64}{space 3}0.000{col 72}{space 4} 1.250113{col 85}{space 3} 2.206081
{txt}{space 30} {c |}
{space 25}_cons {c |}{col 32}{res}{space 2} 1.737184{col 44}{space 2} .1916768{col 55}{space 1}    5.01{col 64}{space 3}0.000{col 72}{space 4} 1.399072{col 85}{space 3} 2.157007
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 3b
. svy: logit AI_directionworse ib3.aiexposure_pizzinelli2023 $controls_demographics if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}  14{txt},{res}   1308{txt}){col 67}= {res}      5.60
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                   AI_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 23}Low Exposure  {c |}{col 38}{res}{space 2} 1.767858{col 50}{space 2} .4504094{col 61}{space 1}    2.24{col 70}{space 3}0.025{col 78}{space 4} 1.072462{col 91}{space 3} 2.914156
{txt}{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.514567{col 50}{space 2} .2338704{col 61}{space 1}    2.69{col 70}{space 3}0.007{col 78}{space 4} 1.118743{col 91}{space 3} 2.050437
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2} 1.023025{col 50}{space 2}  .006361{col 61}{space 1}    3.66{col 70}{space 3}0.000{col 78}{space 4} 1.010622{col 91}{space 3}  1.03558
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} .6892241{col 50}{space 2}  .099762{col 61}{space 1}   -2.57{col 70}{space 3}0.010{col 78}{space 4} .5188479{col 91}{space 3} .9155475
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7158633{col 50}{space 2} .1088969{col 61}{space 1}   -2.20{col 70}{space 3}0.028{col 78}{space 4} .5311619{col 91}{space 3} .9647911
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} .4318928{col 50}{space 2} .0970202{col 61}{space 1}   -3.74{col 70}{space 3}0.000{col 78}{space 4} .2779631{col 91}{space 3} .6710653
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2}  1.29274{col 50}{space 2} .2121279{col 61}{space 1}    1.56{col 70}{space 3}0.118{col 78}{space 4} .9369317{col 91}{space 3} 1.783669
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.328013{col 50}{space 2} .4280008{col 61}{space 1}    0.88{col 70}{space 3}0.379{col 78}{space 4} .7056993{col 91}{space 3} 2.499106
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .8962509{col 50}{space 2} .2379814{col 61}{space 1}   -0.41{col 70}{space 3}0.680{col 78}{space 4} .5323569{col 91}{space 3} 1.508886
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .7679292{col 50}{space 2} .4224479{col 61}{space 1}   -0.48{col 70}{space 3}0.631{col 78}{space 4} .2609965{col 91}{space 3} 2.259476
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}  1.84271{col 50}{space 2} .6284769{col 61}{space 1}    1.79{col 70}{space 3}0.073{col 78}{space 4} .9437973{col 91}{space 3} 3.597784
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 2.097407{col 50}{space 2} .6520903{col 61}{space 1}    2.38{col 70}{space 3}0.017{col 78}{space 4} 1.139722{col 91}{space 3} 3.859816
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.763784{col 50}{space 2} .4975159{col 61}{space 1}    2.01{col 70}{space 3}0.044{col 78}{space 4} 1.014202{col 91}{space 3} 3.067373
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.302754{col 50}{space 2}  .357581{col 61}{space 1}    0.96{col 70}{space 3}0.335{col 78}{space 4} .7603432{col 91}{space 3} 2.232108
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 1.357062{col 50}{space 2} .5756629{col 61}{space 1}    0.72{col 70}{space 3}0.472{col 78}{space 4} .5904602{col 91}{space 3} 3.118952
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 3c
. svy: logit AI_directionworse ib3.aiexposure_pizzinelli2023 $controls_demographics $controls_occupindices if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}  16{txt},{res}   1306{txt}){col 67}= {res}      5.00
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                   AI_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 23}Low Exposure  {c |}{col 38}{res}{space 2} 1.403706{col 50}{space 2} .3958549{col 61}{space 1}    1.20{col 70}{space 3}0.229{col 78}{space 4} .8072555{col 91}{space 3}  2.44085
{txt}{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.338442{col 50}{space 2} .2540643{col 61}{space 1}    1.54{col 70}{space 3}0.125{col 78}{space 4} .9223074{col 91}{space 3} 1.942333
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2}  1.02341{col 50}{space 2}  .006428{col 61}{space 1}    3.68{col 70}{space 3}0.000{col 78}{space 4} 1.010878{col 91}{space 3} 1.036099
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} .7109707{col 50}{space 2} .1034902{col 61}{space 1}   -2.34{col 70}{space 3}0.019{col 78}{space 4} .5343621{col 91}{space 3}  .945949
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7382106{col 50}{space 2} .1141304{col 61}{space 1}   -1.96{col 70}{space 3}0.050{col 78}{space 4} .5450801{col 91}{space 3} .9997704
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} .4494468{col 50}{space 2}  .101425{col 61}{space 1}   -3.54{col 70}{space 3}0.000{col 78}{space 4} .2886786{col 91}{space 3} .6997484
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.298117{col 50}{space 2} .2229151{col 61}{space 1}    1.52{col 70}{space 3}0.129{col 78}{space 4} .9268513{col 91}{space 3} 1.818101
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.297801{col 50}{space 2} .4192695{col 61}{space 1}    0.81{col 70}{space 3}0.420{col 78}{space 4} .6885979{col 91}{space 3} 2.445968
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9778898{col 50}{space 2} .2594475{col 61}{space 1}   -0.08{col 70}{space 3}0.933{col 78}{space 4} .5810954{col 91}{space 3} 1.645631
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .7424778{col 50}{space 2} .4073354{col 61}{space 1}   -0.54{col 70}{space 3}0.587{col 78}{space 4} .2530884{col 91}{space 3} 2.178185
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.823637{col 50}{space 2} .6196246{col 61}{space 1}    1.77{col 70}{space 3}0.077{col 78}{space 4} .9363901{col 91}{space 3} 3.551566
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 2.098247{col 50}{space 2} .6497077{col 61}{space 1}    2.39{col 70}{space 3}0.017{col 78}{space 4}    1.143{col 91}{space 3} 3.851829
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.791512{col 50}{space 2} .5055367{col 61}{space 1}    2.07{col 70}{space 3}0.039{col 78}{space 4} 1.029921{col 91}{space 3} 3.116275
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.364938{col 50}{space 2} .3706768{col 61}{space 1}    1.15{col 70}{space 3}0.252{col 78}{space 4} .8011983{col 91}{space 3} 2.325338
{txt}{space 36} {c |}
{space 23}std_RTI_Autor {c |}{col 38}{res}{space 2} 1.403945{col 50}{space 2} .2588639{col 61}{space 1}    1.84{col 70}{space 3}0.066{col 78}{space 4} .9778196{col 91}{space 3} 2.015771
{txt}{space 9}std_offshorable_Blinder2007 {c |}{col 38}{res}{space 2} .9342975{col 50}{space 2} .0757451{col 61}{space 1}   -0.84{col 70}{space 3}0.402{col 78}{space 4} .7969179{col 91}{space 3}  1.09536
{txt}{space 31}_cons {c |}{col 38}{res}{space 2} 1.400063{col 50}{space 2} .5995317{col 61}{space 1}    0.79{col 70}{space 3}0.432{col 78}{space 4} .6043849{col 91}{space 3} 3.243257
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. 
. ******* TABLE B3 ******
. 
. ** Model 1a
. svy: logit AI_worsen i.aiexposure_gmyrek2023 if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}   3{txt},{res}   3959{txt}){col 67}= {res}     16.83
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}  Linearized
{col 1}              AI_worsen{col 25}{c |} Odds Ratio{col 37}   Std. Err.{col 49}      t{col 57}   P>|t|{col 65}     [95% Con{col 78}f. Interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}aiexposure_gmyrek2023 {c |}
{space 2}Automation Potential  {c |}{col 25}{res}{space 2} 2.149197{col 37}{space 2} .2624887{col 48}{space 1}    6.26{col 57}{space 3}0.000{col 65}{space 4} 1.691548{col 78}{space 3} 2.730663
{txt}Augmentation Potential  {c |}{col 25}{res}{space 2} 1.014396{col 37}{space 2} .1403823{col 48}{space 1}    0.10{col 57}{space 3}0.918{col 65}{space 4}  .773346{col 78}{space 3} 1.330581
{txt}{space 11}Big Unknown  {c |}{col 25}{res}{space 2} 1.563634{col 37}{space 2} .1516716{col 48}{space 1}    4.61{col 57}{space 3}0.000{col 65}{space 4} 1.292837{col 78}{space 3} 1.891152
{txt}{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .2369267{col 37}{space 2} .0171015{col 48}{space 1}  -19.95{col 57}{space 3}0.000{col 65}{space 4} .2056625{col 78}{space 3} .2729437
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 1b
. svy: logit AI_worsen i.aiexposure_gmyrek2023 $controls_demographics if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}  15{txt},{res}   3947{txt}){col 67}= {res}      5.62
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} 2.235553{col 50}{space 2} .2810535{col 61}{space 1}    6.40{col 70}{space 3}0.000{col 78}{space 4} 1.747186{col 91}{space 3} 2.860425
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .9647591{col 50}{space 2} .1345981{col 61}{space 1}   -0.26{col 70}{space 3}0.797{col 78}{space 4}  .733884{col 91}{space 3} 1.268266
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.518108{col 50}{space 2} .1493448{col 61}{space 1}    4.24{col 70}{space 3}0.000{col 78}{space 4} 1.251813{col 91}{space 3}  1.84105
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2} .9920165{col 50}{space 2} .0036112{col 61}{space 1}   -2.20{col 70}{space 3}0.028{col 78}{space 4} .9849617{col 91}{space 3} .9991219
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.162724{col 50}{space 2} .1012922{col 61}{space 1}    1.73{col 70}{space 3}0.084{col 78}{space 4} .9801677{col 91}{space 3} 1.379281
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.360552{col 50}{space 2} .1206814{col 61}{space 1}    3.47{col 70}{space 3}0.001{col 78}{space 4} 1.143379{col 91}{space 3} 1.618975
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} .8090219{col 50}{space 2} .0846058{col 61}{space 1}   -2.03{col 70}{space 3}0.043{col 78}{space 4} .6590468{col 91}{space 3} .9931259
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2}  .978504{col 50}{space 2} .0932267{col 61}{space 1}   -0.23{col 70}{space 3}0.820{col 78}{space 4} .8117829{col 91}{space 3} 1.179466
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9325055{col 50}{space 2} .1705051{col 61}{space 1}   -0.38{col 70}{space 3}0.702{col 78}{space 4} .6515757{col 91}{space 3} 1.334559
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9904221{col 50}{space 2} .1431659{col 61}{space 1}   -0.07{col 70}{space 3}0.947{col 78}{space 4} .7460041{col 91}{space 3}  1.31492
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .7737329{col 50}{space 2} .2196408{col 61}{space 1}   -0.90{col 70}{space 3}0.366{col 78}{space 4} .4434914{col 91}{space 3} 1.349885
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.121236{col 50}{space 2} .1860196{col 61}{space 1}    0.69{col 70}{space 3}0.490{col 78}{space 4} .8099059{col 91}{space 3} 1.552242
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.071204{col 50}{space 2} .1729409{col 61}{space 1}    0.43{col 70}{space 3}0.670{col 78}{space 4} .7805624{col 91}{space 3} 1.470066
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.113069{col 50}{space 2} .1739342{col 61}{space 1}    0.69{col 70}{space 3}0.493{col 78}{space 4} .8193474{col 91}{space 3} 1.512085
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.221336{col 50}{space 2} .1905537{col 61}{space 1}    1.28{col 70}{space 3}0.200{col 78}{space 4} .8994758{col 91}{space 3} 1.658367
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2888535{col 50}{space 2} .0666307{col 61}{space 1}   -5.38{col 70}{space 3}0.000{col 78}{space 4} .1837673{col 91}{space 3} .4540325
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 1c
. svy: logit AI_worsen i.aiexposure_gmyrek2023 $controls_demographics $controls_occupindices if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}  17{txt},{res}   3945{txt}){col 67}= {res}      6.32
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} 1.716403{col 50}{space 2} .2412522{col 61}{space 1}    3.84{col 70}{space 3}0.000{col 78}{space 4} 1.302988{col 91}{space 3} 2.260987
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .9613508{col 50}{space 2} .1357471{col 61}{space 1}   -0.28{col 70}{space 3}0.780{col 78}{space 4} .7288725{col 91}{space 3} 1.267979
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2}  1.42011{col 50}{space 2} .1432865{col 61}{space 1}    3.48{col 70}{space 3}0.001{col 78}{space 4} 1.165228{col 91}{space 3} 1.730744
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2} .9925688{col 50}{space 2} .0036279{col 61}{space 1}   -2.04{col 70}{space 3}0.041{col 78}{space 4} .9854816{col 91}{space 3}  .999707
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.116508{col 50}{space 2} .0985759{col 61}{space 1}    1.25{col 70}{space 3}0.212{col 78}{space 4} .9390456{col 91}{space 3} 1.327507
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.351529{col 50}{space 2} .1213379{col 61}{space 1}    3.36{col 70}{space 3}0.001{col 78}{space 4} 1.133398{col 91}{space 3}  1.61164
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} .7916295{col 50}{space 2}  .083682{col 61}{space 1}   -2.21{col 70}{space 3}0.027{col 78}{space 4} .6434505{col 91}{space 3} .9739322
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2}  1.06931{col 50}{space 2} .1034734{col 61}{space 1}    0.69{col 70}{space 3}0.489{col 78}{space 4} .8845264{col 91}{space 3} 1.292697
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9832211{col 50}{space 2} .1787808{col 61}{space 1}   -0.09{col 70}{space 3}0.926{col 78}{space 4} .6883802{col 91}{space 3} 1.404345
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2}  1.04469{col 50}{space 2} .1500768{col 61}{space 1}    0.30{col 70}{space 3}0.761{col 78}{space 4} .7882594{col 91}{space 3}  1.38454
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8267345{col 50}{space 2} .2366536{col 61}{space 1}   -0.66{col 70}{space 3}0.506{col 78}{space 4} .4716657{col 91}{space 3} 1.449098
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.108164{col 50}{space 2} .1847276{col 61}{space 1}    0.62{col 70}{space 3}0.538{col 78}{space 4} .7992226{col 91}{space 3} 1.536527
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.063498{col 50}{space 2} .1726625{col 61}{space 1}    0.38{col 70}{space 3}0.705{col 78}{space 4} .7735694{col 91}{space 3} 1.462091
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.095743{col 50}{space 2} .1728498{col 61}{space 1}    0.58{col 70}{space 3}0.562{col 78}{space 4} .8042542{col 91}{space 3} 1.492877
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.170663{col 50}{space 2} .1860575{col 61}{space 1}    0.99{col 70}{space 3}0.322{col 78}{space 4} .8572474{col 91}{space 3} 1.598666
{txt}{space 36} {c |}
{space 23}std_RTI_Autor {c |}{col 38}{res}{space 2} 1.127482{col 50}{space 2} .0991354{col 61}{space 1}    1.36{col 70}{space 3}0.172{col 78}{space 4} .9489507{col 91}{space 3} 1.339601
{txt}{space 9}std_offshorable_Blinder2007 {c |}{col 38}{res}{space 2} 1.187852{col 50}{space 2} .0554044{col 61}{space 1}    3.69{col 70}{space 3}0.000{col 78}{space 4} 1.084047{col 91}{space 3} 1.301597
{txt}{space 31}_cons {c |}{col 38}{res}{space 2} .3030875{col 50}{space 2} .0698196{col 61}{space 1}   -5.18{col 70}{space 3}0.000{col 78}{space 4} .1929409{col 91}{space 3} .4761149
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. ** Model 2a
. svy: logit AI_improve i.aiexposure_gmyrek2023 if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}   3{txt},{res}   3959{txt}){col 67}= {res}      6.49
{txt}{col 49}Prob > F{col 67}= {res}    0.0002

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}  Linearized
{col 1}             AI_improve{col 25}{c |} Odds Ratio{col 37}   Std. Err.{col 49}      t{col 57}   P>|t|{col 65}     [95% Con{col 78}f. Interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}aiexposure_gmyrek2023 {c |}
{space 2}Automation Potential  {c |}{col 25}{res}{space 2} .4541972{col 37}{space 2} .1216989{col 48}{space 1}   -2.95{col 57}{space 3}0.003{col 65}{space 4} .2685971{col 78}{space 3} .7680466
{txt}Augmentation Potential  {c |}{col 25}{res}{space 2} 1.303874{col 37}{space 2} .2253171{col 48}{space 1}    1.54{col 57}{space 3}0.125{col 65}{space 4}  .929176{col 78}{space 3} 1.829672
{txt}{space 11}Big Unknown  {c |}{col 25}{res}{space 2}  1.34709{col 37}{space 2} .1873183{col 48}{space 1}    2.14{col 57}{space 3}0.032{col 65}{space 4} 1.025646{col 78}{space 3} 1.769277
{txt}{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .0955351{col 37}{space 2} .0097349{col 48}{space 1}  -23.04{col 57}{space 3}0.000{col 65}{space 4} .0782347{col 78}{space 3} .1166611
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 2b
. svy: logit AI_improve i.aiexposure_gmyrek2023 $controls_demographics if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}  15{txt},{res}   3947{txt}){col 67}= {res}     10.30
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                          AI_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .5323391{col 50}{space 2} .1487025{col 61}{space 1}   -2.26{col 70}{space 3}0.024{col 78}{space 4} .3078526{col 91}{space 3} .9205213
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.245286{col 50}{space 2} .2234259{col 61}{space 1}    1.22{col 70}{space 3}0.222{col 78}{space 4} .8759962{col 91}{space 3} 1.770256
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.241423{col 50}{space 2}  .180464{col 61}{space 1}    1.49{col 70}{space 3}0.137{col 78}{space 4} .9335632{col 91}{space 3} 1.650804
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2} .9687858{col 50}{space 2} .0054905{col 61}{space 1}   -5.60{col 70}{space 3}0.000{col 78}{space 4}  .958081{col 91}{space 3} .9796103
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.612239{col 50}{space 2} .2074193{col 61}{space 1}    3.71{col 70}{space 3}0.000{col 78}{space 4} 1.252814{col 91}{space 3} 2.074782
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.994747{col 50}{space 2} .2766637{col 61}{space 1}    4.98{col 70}{space 3}0.000{col 78}{space 4} 1.519825{col 91}{space 3} 2.618077
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} 2.233064{col 50}{space 2} .4570511{col 61}{space 1}    3.93{col 70}{space 3}0.000{col 78}{space 4} 1.494957{col 91}{space 3} 3.335597
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .7028015{col 50}{space 2} .1029811{col 61}{space 1}   -2.41{col 70}{space 3}0.016{col 78}{space 4}  .527313{col 91}{space 3}  .936692
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .6944568{col 50}{space 2} .1972901{col 61}{space 1}   -1.28{col 70}{space 3}0.199{col 78}{space 4} .3978792{col 91}{space 3} 1.212102
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.094834{col 50}{space 2}  .244923{col 61}{space 1}    0.41{col 70}{space 3}0.685{col 78}{space 4}  .706106{col 91}{space 3} 1.697568
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.050497{col 50}{space 2} .5469275{col 61}{space 1}    0.09{col 70}{space 3}0.925{col 78}{space 4} .3785229{col 91}{space 3} 2.915393
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5319708{col 50}{space 2} .1565196{col 61}{space 1}   -2.15{col 70}{space 3}0.032{col 78}{space 4} .2987897{col 91}{space 3} .9471306
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .4849458{col 50}{space 2} .1328014{col 61}{space 1}   -2.64{col 70}{space 3}0.008{col 78}{space 4} .2834798{col 91}{space 3} .8295914
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .5812188{col 50}{space 2} .1404141{col 61}{space 1}   -2.25{col 70}{space 3}0.025{col 78}{space 4} .3619419{col 91}{space 3} .9333414
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .8512307{col 50}{space 2} .1962051{col 61}{space 1}   -0.70{col 70}{space 3}0.485{col 78}{space 4} .5417375{col 91}{space 3} 1.337536
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .1618173{col 50}{space 2} .0605233{col 61}{space 1}   -4.87{col 70}{space 3}0.000{col 78}{space 4} .0777247{col 91}{space 3} .3368921
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 2c
. svy: logit AI_improve i.aiexposure_gmyrek2023 $controls_demographics $controls_occupindices if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}  17{txt},{res}   3945{txt}){col 67}= {res}     10.28
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                          AI_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .4799791{col 50}{space 2}   .13675{col 61}{space 1}   -2.58{col 70}{space 3}0.010{col 78}{space 4} .2745578{col 91}{space 3} .8390945
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.112817{col 50}{space 2} .2042148{col 61}{space 1}    0.58{col 70}{space 3}0.560{col 78}{space 4} .7765521{col 91}{space 3} 1.594692
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.239504{col 50}{space 2} .1825589{col 61}{space 1}    1.46{col 70}{space 3}0.145{col 78}{space 4} .9286275{col 91}{space 3} 1.654453
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2}  .969126{col 50}{space 2} .0055327{col 61}{space 1}   -5.49{col 70}{space 3}0.000{col 78}{space 4} .9583394{col 91}{space 3} .9800341
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.554656{col 50}{space 2} .2029459{col 61}{space 1}    3.38{col 70}{space 3}0.001{col 78}{space 4} 1.203604{col 91}{space 3} 2.008097
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  1.83961{col 50}{space 2} .2598538{col 61}{space 1}    4.32{col 70}{space 3}0.000{col 78}{space 4}  1.39461{col 91}{space 3} 2.426604
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2}  2.03321{col 50}{space 2} .4195004{col 61}{space 1}    3.44{col 70}{space 3}0.001{col 78}{space 4} 1.356766{col 91}{space 3} 3.046908
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .7646578{col 50}{space 2} .1162988{col 61}{space 1}   -1.76{col 70}{space 3}0.078{col 78}{space 4} .5675003{col 91}{space 3} 1.030311
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .7252594{col 50}{space 2} .2088231{col 61}{space 1}   -1.12{col 70}{space 3}0.265{col 78}{space 4} .4124135{col 91}{space 3} 1.275422
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.102247{col 50}{space 2} .2484272{col 61}{space 1}    0.43{col 70}{space 3}0.666{col 78}{space 4} .7085563{col 91}{space 3} 1.714682
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.185814{col 50}{space 2} .6200118{col 61}{space 1}    0.33{col 70}{space 3}0.744{col 78}{space 4} .4254254{col 91}{space 3} 3.305292
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5248647{col 50}{space 2} .1548365{col 61}{space 1}   -2.19{col 70}{space 3}0.029{col 78}{space 4} .2943499{col 91}{space 3}  .935903
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .4632377{col 50}{space 2} .1276822{col 61}{space 1}   -2.79{col 70}{space 3}0.005{col 78}{space 4} .2698457{col 91}{space 3}  .795229
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .5428763{col 50}{space 2} .1330133{col 61}{space 1}   -2.49{col 70}{space 3}0.013{col 78}{space 4} .3357988{col 91}{space 3} .8776524
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .7368803{col 50}{space 2} .1723107{col 61}{space 1}   -1.31{col 70}{space 3}0.192{col 78}{space 4}    .4659{col 91}{space 3}  1.16547
{txt}{space 36} {c |}
{space 23}std_RTI_Autor {c |}{col 38}{res}{space 2}  .736434{col 50}{space 2} .1093962{col 61}{space 1}   -2.06{col 70}{space 3}0.040{col 78}{space 4} .5503645{col 91}{space 3} .9854106
{txt}{space 9}std_offshorable_Blinder2007 {c |}{col 38}{res}{space 2} 1.276314{col 50}{space 2} .0789251{col 61}{space 1}    3.95{col 70}{space 3}0.000{col 78}{space 4} 1.130589{col 91}{space 3} 1.440823
{txt}{space 31}_cons {c |}{col 38}{res}{space 2} .1853588{col 50}{space 2} .0705107{col 61}{space 1}   -4.43{col 70}{space 3}0.000{col 78}{space 4} .0879258{col 91}{space 3}   .39076
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. ** Model 3a
. svy: logit AI_directionworse ib3.aiexposure_gmyrek2023 if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}   3{txt},{res}   1319{txt}){col 67}= {res}      8.90
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}  Linearized
{col 1}    AI_directionworse{col 23}{c |} Odds Ratio{col 35}   Std. Err.{col 47}      t{col 55}   P>|t|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
aiexposure_gmyrek2023 {c |}
{space 8}Not Affected  {c |}{col 23}{res}{space 2} 1.255641{col 35}{space 2} .2589383{col 46}{space 1}    1.10{col 55}{space 3}0.270{col 63}{space 4} .8378563{col 76}{space 3} 1.881747
{txt}Automation Potential  {c |}{col 23}{res}{space 2} 4.637831{col 35}{space 2} 1.423927{col 46}{space 1}    5.00{col 55}{space 3}0.000{col 63}{space 4} 2.539427{col 76}{space 3} 8.470209
{txt}{space 9}Big Unknown  {c |}{col 23}{res}{space 2} 1.355281{col 35}{space 2} .2705414{col 46}{space 1}    1.52{col 55}{space 3}0.128{col 63}{space 4} .9161292{col 76}{space 3} 2.004944
{txt}{space 21} {c |}
{space 16}_cons {c |}{col 23}{res}{space 2} 1.749315{col 35}{space 2}  .297136{col 46}{space 1}    3.29{col 55}{space 3}0.001{col 63}{space 4} 1.253579{col 76}{space 3} 2.441094
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 3b
. svy: logit AI_directionworse ib3.aiexposure_gmyrek2023 $controls_demographics if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}  15{txt},{res}   1307{txt}){col 67}= {res}      5.59
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                   AI_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}aiexposure_gmyrek2023 {c |}
{space 23}Not Affected  {c |}{col 38}{res}{space 2} 1.162931{col 50}{space 2} .2487439{col 61}{space 1}    0.71{col 70}{space 3}0.481{col 78}{space 4} .7643994{col 91}{space 3} 1.769245
{txt}{space 15}Automation Potential  {c |}{col 38}{res}{space 2}   3.4273{col 50}{space 2} 1.077631{col 61}{space 1}    3.92{col 70}{space 3}0.000{col 78}{space 4} 1.849554{col 91}{space 3} 6.350929
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.192179{col 50}{space 2} .2487263{col 61}{space 1}    0.84{col 70}{space 3}0.400{col 78}{space 4} .7917553{col 91}{space 3} 1.795114
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2} 1.021116{col 50}{space 2} .0063223{col 61}{space 1}    3.37{col 70}{space 3}0.001{col 78}{space 4} 1.008788{col 91}{space 3} 1.033595
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} .7479096{col 50}{space 2} .1096777{col 61}{space 1}   -1.98{col 70}{space 3}0.048{col 78}{space 4} .5609312{col 91}{space 3} .9972145
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .6906185{col 50}{space 2} .1055439{col 61}{space 1}   -2.42{col 70}{space 3}0.016{col 78}{space 4} .5117218{col 91}{space 3}  .932057
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} .4114719{col 50}{space 2} .0936857{col 61}{space 1}   -3.90{col 70}{space 3}0.000{col 78}{space 4} .2632432{col 91}{space 3} .6431663
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.302644{col 50}{space 2} .2152886{col 61}{space 1}    1.60{col 70}{space 3}0.110{col 78}{space 4} .9419292{col 91}{space 3} 1.801496
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.273158{col 50}{space 2} .4239843{col 61}{space 1}    0.73{col 70}{space 3}0.468{col 78}{space 4} .6624557{col 91}{space 3} 2.446851
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9312545{col 50}{space 2} .2473848{col 61}{space 1}   -0.27{col 70}{space 3}0.789{col 78}{space 4} .5530216{col 91}{space 3} 1.568176
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .7078748{col 50}{space 2} .4404368{col 61}{space 1}   -0.56{col 70}{space 3}0.579{col 78}{space 4} .2088607{col 91}{space 3} 2.399144
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.669033{col 50}{space 2} .5691417{col 61}{space 1}    1.50{col 70}{space 3}0.133{col 78}{space 4} .8549455{col 91}{space 3} 3.258304
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.908685{col 50}{space 2} .6007226{col 61}{space 1}    2.05{col 70}{space 3}0.040{col 78}{space 4} 1.029411{col 91}{space 3} 3.538992
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.635793{col 50}{space 2} .4600001{col 61}{space 1}    1.75{col 70}{space 3}0.080{col 78}{space 4} .9421996{col 91}{space 3} 2.839969
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.244886{col 50}{space 2} .3377145{col 61}{space 1}    0.81{col 70}{space 3}0.420{col 78}{space 4} .7311437{col 91}{space 3} 2.119613
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 1.653349{col 50}{space 2} .7036526{col 61}{space 1}    1.18{col 70}{space 3}0.238{col 78}{space 4} .7174104{col 91}{space 3} 3.810321
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 3c
. svy: logit AI_directionworse ib3.aiexposure_gmyrek2023 $controls_demographics $controls_occupindices if infullmodel_Table1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}  17{txt},{res}   1305{txt}){col 67}= {res}      5.22
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                   AI_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}aiexposure_gmyrek2023 {c |}
{space 23}Not Affected  {c |}{col 38}{res}{space 2} 1.095452{col 50}{space 2} .2361537{col 61}{space 1}    0.42{col 70}{space 3}0.672{col 78}{space 4} .7176722{col 91}{space 3} 1.672094
{txt}{space 15}Automation Potential  {c |}{col 38}{res}{space 2} 3.004289{col 50}{space 2} .9782146{col 61}{space 1}    3.38{col 70}{space 3}0.001{col 78}{space 4} 1.586102{col 91}{space 3} 5.690524
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.067489{col 50}{space 2} .2290692{col 61}{space 1}    0.30{col 70}{space 3}0.761{col 78}{space 4} .7007115{col 91}{space 3} 1.626251
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2} 1.022007{col 50}{space 2} .0064316{col 61}{space 1}    3.46{col 70}{space 3}0.001{col 78}{space 4} 1.009468{col 91}{space 3} 1.034703
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} .7628162{col 50}{space 2} .1117754{col 61}{space 1}   -1.85{col 70}{space 3}0.065{col 78}{space 4} .5722411{col 91}{space 3} 1.016859
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7353778{col 50}{space 2} .1151349{col 61}{space 1}   -1.96{col 70}{space 3}0.050{col 78}{space 4} .5409029{col 91}{space 3} .9997736
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} .4415403{col 50}{space 2} .1016079{col 61}{space 1}   -3.55{col 70}{space 3}0.000{col 78}{space 4} .2811325{col 91}{space 3} .6934732
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.293344{col 50}{space 2} .2240005{col 61}{space 1}    1.49{col 70}{space 3}0.138{col 78}{space 4}  .920778{col 91}{space 3} 1.816657
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}  1.25701{col 50}{space 2} .4161744{col 61}{space 1}    0.69{col 70}{space 3}0.490{col 78}{space 4} .6565416{col 91}{space 3} 2.406664
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.012727{col 50}{space 2}  .268786{col 61}{space 1}    0.05{col 70}{space 3}0.962{col 78}{space 4} .6016856{col 91}{space 3} 1.704572
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2}  .686056{col 50}{space 2} .4217752{col 61}{space 1}   -0.61{col 70}{space 3}0.540{col 78}{space 4} .2053884{col 91}{space 3} 2.291623
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.689433{col 50}{space 2} .5707858{col 61}{space 1}    1.55{col 70}{space 3}0.121{col 78}{space 4} .8707495{col 91}{space 3} 3.277846
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.972542{col 50}{space 2} .6154734{col 61}{space 1}    2.18{col 70}{space 3}0.030{col 78}{space 4} 1.069524{col 91}{space 3} 3.637995
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.736293{col 50}{space 2} .4882026{col 61}{space 1}    1.96{col 70}{space 3}0.050{col 78}{space 4} 1.000154{col 91}{space 3}  3.01425
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2}  1.37574{col 50}{space 2} .3704561{col 61}{space 1}    1.18{col 70}{space 3}0.236{col 78}{space 4} .8111785{col 91}{space 3} 2.333222
{txt}{space 36} {c |}
{space 23}std_RTI_Autor {c |}{col 38}{res}{space 2} 1.424456{col 50}{space 2} .2338757{col 61}{space 1}    2.15{col 70}{space 3}0.031{col 78}{space 4} 1.032204{col 91}{space 3}  1.96577
{txt}{space 9}std_offshorable_Blinder2007 {c |}{col 38}{res}{space 2} .9027133{col 50}{space 2} .0648006{col 61}{space 1}   -1.43{col 70}{space 3}0.154{col 78}{space 4} .7841353{col 91}{space 3} 1.039223
{txt}{space 31}_cons {c |}{col 38}{res}{space 2} 1.539998{col 50}{space 2} .6672614{col 61}{space 1}    1.00{col 70}{space 3}0.319{col 78}{space 4} .6582141{col 91}{space 3} 3.603073
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. 
. ************************************************************************************************************
. 
. * APPENDIX C: Replication of Table 1 with Additional ISCO-08 Occupational Group Control
. 
. * TABLE C1: Felten et al. (2021) Measure
.         
. * Set Controls and Sample Size  
. global controls_occupindices std_RTI_Autor std_offshorable_Blinder2007 
{txt}
{com}. global controls_demographics age ib1.female_dummy i.degree_dummy ib2.employmentstatus5 i.employsector5 i.equivincomeHHquintile_HBAI_imp i.isco08_10group        
{txt}
{com}.  
. svy: logit AI_worsen aiexposure_felten2021 $controls_demographics  $controls_occupindices, or
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}  23{txt},{res}   3939{txt}){col 67}= {res}      4.71
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 44}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 45}{c |}{col 57}  Linearized
{col 1}                                  AI_worsen{col 45}{c |} Odds Ratio{col 57}   Std. Err.{col 69}      t{col 77}   P>|t|{col 85}     [95% Con{col 98}f. Interval]
{hline 44}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}aiexposure_felten2021 {c |}{col 45}{res}{space 2} 1.267953{col 57}{space 2} .1059723{col 68}{space 1}    2.84{col 77}{space 3}0.005{col 85}{space 4} 1.076316{col 98}{space 3} 1.493709
{txt}{space 40}age {c |}{col 45}{res}{space 2} .9932252{col 57}{space 2} .0036402{col 68}{space 1}   -1.85{col 77}{space 3}0.064{col 85}{space 4}  .986114{col 98}{space 3} 1.000388
{txt}{space 43} {c |}
{space 31}female_dummy {c |}
{space 38}Male  {c |}{col 45}{res}{space 2} 1.140911{col 57}{space 2}  .101996{col 68}{space 1}    1.47{col 77}{space 3}0.140{col 85}{space 4} .9574854{col 98}{space 3} 1.359475
{txt}{space 43} {c |}
{space 31}degree_dummy {c |}
{space 29}Degree-Holder  {c |}{col 45}{res}{space 2} 1.262006{col 57}{space 2} .1161852{col 68}{space 1}    2.53{col 77}{space 3}0.012{col 85}{space 4} 1.053592{col 98}{space 3} 1.511646
{txt}{space 43} {c |}
{space 26}employmentstatus5 {c |}
{space 33}FT Worker  {c |}{col 45}{res}{space 2} .7882795{col 57}{space 2} .0837285{col 68}{space 1}   -2.24{col 77}{space 3}0.025{col 85}{space 4} .6400895{col 98}{space 3} .9707776
{txt}{space 43} {c |}
{space 30}employsector5 {c |}
{space 20}Public Sector Employee  {c |}{col 45}{res}{space 2} 1.028549{col 57}{space 2} .1026552{col 68}{space 1}    0.28{col 77}{space 3}0.778{col 85}{space 4} .8457543{col 98}{space 3} 1.250852
{txt}{space 7}Charity / Voluntary Sector Employee  {c |}{col 45}{res}{space 2} .9623401{col 57}{space 2} .1733326{col 68}{space 1}   -0.21{col 77}{space 3}0.831{col 85}{space 4} .6760315{col 98}{space 3} 1.369904
{txt}{space 13}Self-Employed / Company Owner  {c |}{col 45}{res}{space 2} 1.109237{col 57}{space 2} .1620265{col 68}{space 1}    0.71{col 77}{space 3}0.478{col 85}{space 4}  .833012{col 98}{space 3} 1.477058
{txt}{space 32}Other / DK  {c |}{col 45}{res}{space 2} .8529034{col 57}{space 2} .2428662{col 68}{space 1}   -0.56{col 77}{space 3}0.576{col 85}{space 4} .4880274{col 98}{space 3} 1.490581
{txt}{space 43} {c |}
{space 13}equivincomeHHquintile_HBAI_imp {c |}
{space 41}2  {c |}{col 45}{res}{space 2}  1.09629{col 57}{space 2} .1843957{col 68}{space 1}    0.55{col 77}{space 3}0.585{col 85}{space 4} .7883332{col 98}{space 3} 1.524548
{txt}{space 41}3  {c |}{col 45}{res}{space 2} 1.007751{col 57}{space 2} .1639771{col 68}{space 1}    0.05{col 77}{space 3}0.962{col 85}{space 4} .7324989{col 98}{space 3} 1.386436
{txt}{space 41}4  {c |}{col 45}{res}{space 2} 1.010472{col 57}{space 2} .1611961{col 68}{space 1}    0.07{col 77}{space 3}0.948{col 85}{space 4} .7390849{col 98}{space 3}  1.38151
{txt}{space 30}Top Quintile  {c |}{col 45}{res}{space 2} 1.039365{col 57}{space 2} .1679306{col 68}{space 1}    0.24{col 77}{space 3}0.811{col 85}{space 4} .7571763{col 98}{space 3} 1.426721
{txt}{space 43} {c |}
{space 29}isco08_10group {c |}
{space 29}Professionals  {c |}{col 45}{res}{space 2} 1.274942{col 57}{space 2} .2130442{col 68}{space 1}    1.45{col 77}{space 3}0.146{col 85}{space 4} .9187772{col 98}{space 3} 1.769174
{txt}{space 3}Technicians and Associate Professionals  {c |}{col 45}{res}{space 2} .9609764{col 57}{space 2} .2036006{col 68}{space 1}   -0.19{col 77}{space 3}0.851{col 85}{space 4} .6343294{col 98}{space 3}  1.45583
{txt}{space 18}Clerical Support Workers  {c |}{col 45}{res}{space 2} 1.192608{col 57}{space 2} .2631709{col 68}{space 1}    0.80{col 77}{space 3}0.425{col 85}{space 4} .7737612{col 98}{space 3} 1.838181
{txt}{space 17}Service and Sales Workers  {c |}{col 45}{res}{space 2} .9140526{col 57}{space 2} .2046647{col 68}{space 1}   -0.40{col 77}{space 3}0.688{col 85}{space 4} .5892793{col 98}{space 3}  1.41782
{txt}{space 14}Skilled Agricultural Workers  {c |}{col 45}{res}{space 2} .3106662{col 57}{space 2} .2577221{col 68}{space 1}   -1.41{col 77}{space 3}0.159{col 85}{space 4} .0610857{col 98}{space 3} 1.579969
{txt}{space 10}Craft and Related Trades Workers  {c |}{col 45}{res}{space 2} .9167721{col 57}{space 2} .3346923{col 68}{space 1}   -0.24{col 77}{space 3}0.812{col 85}{space 4} .4481386{col 98}{space 3} 1.875471
{txt}Plant and Machine Operators and Assemblers  {c |}{col 45}{res}{space 2} .8340897{col 57}{space 2} .3102109{col 68}{space 1}   -0.49{col 77}{space 3}0.626{col 85}{space 4} .4022916{col 98}{space 3} 1.729357
{txt}{space 20}Elementary Occupations  {c |}{col 45}{res}{space 2} .7986796{col 57}{space 2} .2644476{col 68}{space 1}   -0.68{col 77}{space 3}0.497{col 85}{space 4} .4173002{col 98}{space 3}  1.52861
{txt}{space 43} {c |}
{space 30}std_RTI_Autor {c |}{col 45}{res}{space 2} 1.573903{col 57}{space 2} .1900598{col 68}{space 1}    3.76{col 77}{space 3}0.000{col 85}{space 4} 1.242104{col 98}{space 3} 1.994334
{txt}{space 16}std_offshorable_Blinder2007 {c |}{col 45}{res}{space 2}  1.04515{col 57}{space 2}  .054217{col 68}{space 1}    0.85{col 77}{space 3}0.395{col 85}{space 4} .9440806{col 98}{space 3} 1.157039
{txt}{space 38}_cons {c |}{col 45}{res}{space 2} .3333901{col 57}{space 2} .0970848{col 68}{space 1}   -3.77{col 77}{space 3}0.000{col 85}{space 4} .1883662{col 98}{space 3} .5900685
{txt}{hline 44}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. generate infullmodel_TableC1 = e(sample) 
{txt}
{com}.          
.          
. ******* TABLE C1 ******
. 
. ** Model 1a
. svy: logit AI_worsen aiexposure_felten2021 if infullmodel_TableC1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}   1{txt},{res}   3961{txt}){col 67}= {res}     42.23
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}  Linearized
{col 1}            AI_worsen{col 23}{c |} Odds Ratio{col 35}   Std. Err.{col 47}      t{col 55}   P>|t|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
aiexposure_felten2021 {c |}{col 23}{res}{space 2} 1.418414{col 35}{space 2} .0762961{col 46}{space 1}    6.50{col 55}{space 3}0.000{col 63}{space 4} 1.276448{col 76}{space 3} 1.576169
{txt}{space 16}_cons {c |}{col 23}{res}{space 2} .2546991{col 35}{space 2} .0140968{col 46}{space 1}  -24.71{col 55}{space 3}0.000{col 63}{space 4}  .228508{col 76}{space 3} .2838921
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 1b
. svy: logit AI_worsen aiexposure_felten2021 i.isco08_10group if infullmodel_TableC1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}   9{txt},{res}   3953{txt}){col 67}= {res}      7.51
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 44}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 45}{c |}{col 57}  Linearized
{col 1}                                  AI_worsen{col 45}{c |} Odds Ratio{col 57}   Std. Err.{col 69}      t{col 77}   P>|t|{col 85}     [95% Con{col 98}f. Interval]
{hline 44}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}aiexposure_felten2021 {c |}{col 45}{res}{space 2} 1.304126{col 57}{space 2} .1032257{col 68}{space 1}    3.35{col 77}{space 3}0.001{col 85}{space 4} 1.116667{col 98}{space 3} 1.523054
{txt}{space 43} {c |}
{space 29}isco08_10group {c |}
{space 29}Professionals  {c |}{col 45}{res}{space 2} 1.486318{col 57}{space 2} .2436365{col 68}{space 1}    2.42{col 77}{space 3}0.016{col 85}{space 4} 1.077806{col 98}{space 3} 2.049665
{txt}{space 3}Technicians and Associate Professionals  {c |}{col 45}{res}{space 2} 1.456744{col 57}{space 2} .2558228{col 68}{space 1}    2.14{col 77}{space 3}0.032{col 85}{space 4} 1.032418{col 98}{space 3} 2.055468
{txt}{space 18}Clerical Support Workers  {c |}{col 45}{res}{space 2} 1.934998{col 57}{space 2} .3360222{col 68}{space 1}    3.80{col 77}{space 3}0.000{col 85}{space 4} 1.376638{col 98}{space 3} 2.719826
{txt}{space 17}Service and Sales Workers  {c |}{col 45}{res}{space 2} 1.076125{col 57}{space 2} .2254224{col 68}{space 1}    0.35{col 77}{space 3}0.726{col 85}{space 4} .7136771{col 98}{space 3} 1.622645
{txt}{space 14}Skilled Agricultural Workers  {c |}{col 45}{res}{space 2} .4360212{col 57}{space 2} .3549937{col 68}{space 1}   -1.02{col 77}{space 3}0.308{col 85}{space 4} .0883642{col 98}{space 3} 2.151488
{txt}{space 10}Craft and Related Trades Workers  {c |}{col 45}{res}{space 2} 1.242955{col 57}{space 2}  .422236{col 68}{space 1}    0.64{col 77}{space 3}0.522{col 85}{space 4} .6385739{col 98}{space 3} 2.419357
{txt}Plant and Machine Operators and Assemblers  {c |}{col 45}{res}{space 2} 1.253515{col 57}{space 2} .4377896{col 68}{space 1}    0.65{col 77}{space 3}0.518{col 85}{space 4} .6320581{col 98}{space 3} 2.486006
{txt}{space 20}Elementary Occupations  {c |}{col 45}{res}{space 2}  1.45657{col 57}{space 2} .4296748{col 68}{space 1}    1.27{col 77}{space 3}0.202{col 85}{space 4} .8168791{col 98}{space 3} 2.597196
{txt}{space 43} {c |}
{space 38}_cons {c |}{col 45}{res}{space 2} .1885155{col 57}{space 2}  .031524{col 68}{space 1}   -9.98{col 77}{space 3}0.000{col 85}{space 4} .1358201{col 98}{space 3} .2616558
{txt}{hline 44}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 1c
. svy: logit AI_worsen aiexposure_felten2021 $controls_demographics $controls_occupindices if infullmodel_TableC1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}  23{txt},{res}   3939{txt}){col 67}= {res}      4.71
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 44}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 45}{c |}{col 57}  Linearized
{col 1}                                  AI_worsen{col 45}{c |} Odds Ratio{col 57}   Std. Err.{col 69}      t{col 77}   P>|t|{col 85}     [95% Con{col 98}f. Interval]
{hline 44}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}aiexposure_felten2021 {c |}{col 45}{res}{space 2} 1.267953{col 57}{space 2} .1059723{col 68}{space 1}    2.84{col 77}{space 3}0.005{col 85}{space 4} 1.076316{col 98}{space 3} 1.493709
{txt}{space 40}age {c |}{col 45}{res}{space 2} .9932252{col 57}{space 2} .0036402{col 68}{space 1}   -1.85{col 77}{space 3}0.064{col 85}{space 4}  .986114{col 98}{space 3} 1.000388
{txt}{space 43} {c |}
{space 31}female_dummy {c |}
{space 38}Male  {c |}{col 45}{res}{space 2} 1.140911{col 57}{space 2}  .101996{col 68}{space 1}    1.47{col 77}{space 3}0.140{col 85}{space 4} .9574854{col 98}{space 3} 1.359475
{txt}{space 43} {c |}
{space 31}degree_dummy {c |}
{space 29}Degree-Holder  {c |}{col 45}{res}{space 2} 1.262006{col 57}{space 2} .1161852{col 68}{space 1}    2.53{col 77}{space 3}0.012{col 85}{space 4} 1.053592{col 98}{space 3} 1.511646
{txt}{space 43} {c |}
{space 26}employmentstatus5 {c |}
{space 33}FT Worker  {c |}{col 45}{res}{space 2} .7882795{col 57}{space 2} .0837285{col 68}{space 1}   -2.24{col 77}{space 3}0.025{col 85}{space 4} .6400895{col 98}{space 3} .9707776
{txt}{space 43} {c |}
{space 30}employsector5 {c |}
{space 20}Public Sector Employee  {c |}{col 45}{res}{space 2} 1.028549{col 57}{space 2} .1026552{col 68}{space 1}    0.28{col 77}{space 3}0.778{col 85}{space 4} .8457543{col 98}{space 3} 1.250852
{txt}{space 7}Charity / Voluntary Sector Employee  {c |}{col 45}{res}{space 2} .9623401{col 57}{space 2} .1733326{col 68}{space 1}   -0.21{col 77}{space 3}0.831{col 85}{space 4} .6760315{col 98}{space 3} 1.369904
{txt}{space 13}Self-Employed / Company Owner  {c |}{col 45}{res}{space 2} 1.109237{col 57}{space 2} .1620265{col 68}{space 1}    0.71{col 77}{space 3}0.478{col 85}{space 4}  .833012{col 98}{space 3} 1.477058
{txt}{space 32}Other / DK  {c |}{col 45}{res}{space 2} .8529034{col 57}{space 2} .2428662{col 68}{space 1}   -0.56{col 77}{space 3}0.576{col 85}{space 4} .4880274{col 98}{space 3} 1.490581
{txt}{space 43} {c |}
{space 13}equivincomeHHquintile_HBAI_imp {c |}
{space 41}2  {c |}{col 45}{res}{space 2}  1.09629{col 57}{space 2} .1843957{col 68}{space 1}    0.55{col 77}{space 3}0.585{col 85}{space 4} .7883332{col 98}{space 3} 1.524548
{txt}{space 41}3  {c |}{col 45}{res}{space 2} 1.007751{col 57}{space 2} .1639771{col 68}{space 1}    0.05{col 77}{space 3}0.962{col 85}{space 4} .7324989{col 98}{space 3} 1.386436
{txt}{space 41}4  {c |}{col 45}{res}{space 2} 1.010472{col 57}{space 2} .1611961{col 68}{space 1}    0.07{col 77}{space 3}0.948{col 85}{space 4} .7390849{col 98}{space 3}  1.38151
{txt}{space 30}Top Quintile  {c |}{col 45}{res}{space 2} 1.039365{col 57}{space 2} .1679306{col 68}{space 1}    0.24{col 77}{space 3}0.811{col 85}{space 4} .7571763{col 98}{space 3} 1.426721
{txt}{space 43} {c |}
{space 29}isco08_10group {c |}
{space 29}Professionals  {c |}{col 45}{res}{space 2} 1.274942{col 57}{space 2} .2130442{col 68}{space 1}    1.45{col 77}{space 3}0.146{col 85}{space 4} .9187772{col 98}{space 3} 1.769174
{txt}{space 3}Technicians and Associate Professionals  {c |}{col 45}{res}{space 2} .9609764{col 57}{space 2} .2036006{col 68}{space 1}   -0.19{col 77}{space 3}0.851{col 85}{space 4} .6343294{col 98}{space 3}  1.45583
{txt}{space 18}Clerical Support Workers  {c |}{col 45}{res}{space 2} 1.192608{col 57}{space 2} .2631709{col 68}{space 1}    0.80{col 77}{space 3}0.425{col 85}{space 4} .7737612{col 98}{space 3} 1.838181
{txt}{space 17}Service and Sales Workers  {c |}{col 45}{res}{space 2} .9140526{col 57}{space 2} .2046647{col 68}{space 1}   -0.40{col 77}{space 3}0.688{col 85}{space 4} .5892793{col 98}{space 3}  1.41782
{txt}{space 14}Skilled Agricultural Workers  {c |}{col 45}{res}{space 2} .3106662{col 57}{space 2} .2577221{col 68}{space 1}   -1.41{col 77}{space 3}0.159{col 85}{space 4} .0610857{col 98}{space 3} 1.579969
{txt}{space 10}Craft and Related Trades Workers  {c |}{col 45}{res}{space 2} .9167721{col 57}{space 2} .3346923{col 68}{space 1}   -0.24{col 77}{space 3}0.812{col 85}{space 4} .4481386{col 98}{space 3} 1.875471
{txt}Plant and Machine Operators and Assemblers  {c |}{col 45}{res}{space 2} .8340897{col 57}{space 2} .3102109{col 68}{space 1}   -0.49{col 77}{space 3}0.626{col 85}{space 4} .4022916{col 98}{space 3} 1.729357
{txt}{space 20}Elementary Occupations  {c |}{col 45}{res}{space 2} .7986796{col 57}{space 2} .2644476{col 68}{space 1}   -0.68{col 77}{space 3}0.497{col 85}{space 4} .4173002{col 98}{space 3}  1.52861
{txt}{space 43} {c |}
{space 30}std_RTI_Autor {c |}{col 45}{res}{space 2} 1.573903{col 57}{space 2} .1900598{col 68}{space 1}    3.76{col 77}{space 3}0.000{col 85}{space 4} 1.242104{col 98}{space 3} 1.994334
{txt}{space 16}std_offshorable_Blinder2007 {c |}{col 45}{res}{space 2}  1.04515{col 57}{space 2}  .054217{col 68}{space 1}    0.85{col 77}{space 3}0.395{col 85}{space 4} .9440806{col 98}{space 3} 1.157039
{txt}{space 38}_cons {c |}{col 45}{res}{space 2} .3333901{col 57}{space 2} .0970848{col 68}{space 1}   -3.77{col 77}{space 3}0.000{col 85}{space 4} .1883662{col 98}{space 3} .5900685
{txt}{hline 44}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. ** Model 2a
. svy: logit AI_improve aiexposure_felten2021 if infullmodel_TableC1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}   1{txt},{res}   3961{txt}){col 67}= {res}     37.96
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}  Linearized
{col 1}           AI_improve{col 23}{c |} Odds Ratio{col 35}   Std. Err.{col 47}      t{col 55}   P>|t|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
aiexposure_felten2021 {c |}{col 23}{res}{space 2} 1.842481{col 35}{space 2} .1827491{col 46}{space 1}    6.16{col 55}{space 3}0.000{col 63}{space 4} 1.516874{col 76}{space 3} 2.237981
{txt}{space 16}_cons {c |}{col 23}{res}{space 2} .0691515{col 35}{space 2} .0072462{col 46}{space 1}  -25.49{col 55}{space 3}0.000{col 63}{space 4} .0563091{col 76}{space 3} .0849228
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 2b
. svy: logit AI_improve aiexposure_felten2021 i.isco08_10group if infullmodel_TableC1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}note: 6.isco08_10group != 0 predicts failure perfectly
      6.isco08_10group dropped and 25 obs not used

Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,937
{txt}{col 1}Number of PSUs{col 20}= {res}    3,937{txt}{col 49}Population size{col 67}={res} 4,053.6686
{txt}{col 49}Design df{col 67}= {res}     3,936
{txt}{col 49}F({res}   8{txt},{res}   3929{txt}){col 67}= {res}      9.37
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 44}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 45}{c |}{col 57}  Linearized
{col 1}                                 AI_improve{col 45}{c |} Odds Ratio{col 57}   Std. Err.{col 69}      t{col 77}   P>|t|{col 85}     [95% Con{col 98}f. Interval]
{hline 44}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}aiexposure_felten2021 {c |}{col 45}{res}{space 2}   1.7028{col 57}{space 2} .2254174{col 68}{space 1}    4.02{col 77}{space 3}0.000{col 85}{space 4}  1.31355{col 98}{space 3} 2.207399
{txt}{space 43} {c |}
{space 29}isco08_10group {c |}
{space 29}Professionals  {c |}{col 45}{res}{space 2} .9598311{col 57}{space 2} .1824647{col 68}{space 1}   -0.22{col 77}{space 3}0.829{col 85}{space 4} .6611971{col 98}{space 3} 1.393345
{txt}{space 3}Technicians and Associate Professionals  {c |}{col 45}{res}{space 2}  .790948{col 57}{space 2} .1736154{col 68}{space 1}   -1.07{col 77}{space 3}0.285{col 85}{space 4} .5143388{col 98}{space 3} 1.216316
{txt}{space 18}Clerical Support Workers  {c |}{col 45}{res}{space 2} .3326189{col 57}{space 2} .0884395{col 68}{space 1}   -4.14{col 77}{space 3}0.000{col 85}{space 4} .1974934{col 98}{space 3} .5601976
{txt}{space 17}Service and Sales Workers  {c |}{col 45}{res}{space 2}  .487002{col 57}{space 2} .1708628{col 68}{space 1}   -2.05{col 77}{space 3}0.040{col 85}{space 4} .2447925{col 98}{space 3} .9688654
{txt}{space 14}Skilled Agricultural Workers  {c |}{col 45}{res}{space 2}        1{col 57}{txt}  (empty)
{space 10}Craft and Related Trades Workers  {c |}{col 45}{res}{space 2}  .681388{col 57}{space 2}  .319138{col 68}{space 1}   -0.82{col 77}{space 3}0.413{col 85}{space 4} .2720189{col 98}{space 3} 1.706828
{txt}Plant and Machine Operators and Assemblers  {c |}{col 45}{res}{space 2} .9984211{col 57}{space 2} .5798869{col 68}{space 1}   -0.00{col 77}{space 3}0.998{col 85}{space 4} .3197279{col 98}{space 3} 3.117791
{txt}{space 20}Elementary Occupations  {c |}{col 45}{res}{space 2}  .830123{col 57}{space 2}  .401792{col 68}{space 1}   -0.38{col 77}{space 3}0.701{col 85}{space 4} .3213819{col 98}{space 3} 2.144191
{txt}{space 43} {c |}
{space 38}_cons {c |}{col 45}{res}{space 2} .0990168{col 57}{space 2} .0218909{col 68}{space 1}  -10.46{col 77}{space 3}0.000{col 85}{space 4} .0641897{col 98}{space 3} .1527398
{txt}{hline 44}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 2c
. svy: logit AI_improve aiexposure_felten2021 $controls_demographics $controls_occupindices if infullmodel_TableC1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}note: 6.isco08_10group != 0 predicts failure perfectly
      6.isco08_10group dropped and 25 obs not used

Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,937
{txt}{col 1}Number of PSUs{col 20}= {res}    3,937{txt}{col 49}Population size{col 67}={res} 4,053.6686
{txt}{col 49}Design df{col 67}= {res}     3,936
{txt}{col 49}F({res}  22{txt},{res}   3915{txt}){col 67}= {res}      8.54
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 44}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 45}{c |}{col 57}  Linearized
{col 1}                                 AI_improve{col 45}{c |} Odds Ratio{col 57}   Std. Err.{col 69}      t{col 77}   P>|t|{col 85}     [95% Con{col 98}f. Interval]
{hline 44}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}aiexposure_felten2021 {c |}{col 45}{res}{space 2} 1.490704{col 57}{space 2} .2122358{col 68}{space 1}    2.80{col 77}{space 3}0.005{col 85}{space 4} 1.127628{col 98}{space 3} 1.970682
{txt}{space 40}age {c |}{col 45}{res}{space 2} .9693598{col 57}{space 2} .0056573{col 68}{space 1}   -5.33{col 77}{space 3}0.000{col 85}{space 4} .9583316{col 98}{space 3}  .980515
{txt}{space 43} {c |}
{space 31}female_dummy {c |}
{space 38}Male  {c |}{col 45}{res}{space 2} 1.616223{col 57}{space 2} .2118315{col 68}{space 1}    3.66{col 77}{space 3}0.000{col 85}{space 4} 1.249982{col 98}{space 3} 2.089773
{txt}{space 43} {c |}
{space 31}degree_dummy {c |}
{space 29}Degree-Holder  {c |}{col 45}{res}{space 2} 1.672236{col 57}{space 2} .2304591{col 68}{space 1}    3.73{col 77}{space 3}0.000{col 85}{space 4} 1.276301{col 98}{space 3} 2.190998
{txt}{space 43} {c |}
{space 26}employmentstatus5 {c |}
{space 33}FT Worker  {c |}{col 45}{res}{space 2} 2.021014{col 57}{space 2} .4198051{col 68}{space 1}    3.39{col 77}{space 3}0.001{col 85}{space 4} 1.344941{col 98}{space 3} 3.036934
{txt}{space 43} {c |}
{space 30}employsector5 {c |}
{space 20}Public Sector Employee  {c |}{col 45}{res}{space 2} .7894642{col 57}{space 2} .1212884{col 68}{space 1}   -1.54{col 77}{space 3}0.124{col 85}{space 4} .5841427{col 98}{space 3} 1.066955
{txt}{space 7}Charity / Voluntary Sector Employee  {c |}{col 45}{res}{space 2} .7355302{col 57}{space 2} .2099374{col 68}{space 1}   -1.08{col 77}{space 3}0.282{col 85}{space 4} .4203133{col 98}{space 3} 1.287146
{txt}{space 13}Self-Employed / Company Owner  {c |}{col 45}{res}{space 2} 1.207659{col 57}{space 2} .2783766{col 68}{space 1}    0.82{col 77}{space 3}0.413{col 85}{space 4} .7685533{col 98}{space 3} 1.897643
{txt}{space 32}Other / DK  {c |}{col 45}{res}{space 2} 1.270695{col 57}{space 2}  .667354{col 68}{space 1}    0.46{col 77}{space 3}0.648{col 85}{space 4} .4537985{col 98}{space 3} 3.558114
{txt}{space 43} {c |}
{space 13}equivincomeHHquintile_HBAI_imp {c |}
{space 41}2  {c |}{col 45}{res}{space 2}  .503869{col 57}{space 2} .1500469{col 68}{space 1}   -2.30{col 77}{space 3}0.021{col 85}{space 4} .2810352{col 98}{space 3} .9033884
{txt}{space 41}3  {c |}{col 45}{res}{space 2} .4207882{col 57}{space 2} .1158856{col 68}{space 1}   -3.14{col 77}{space 3}0.002{col 85}{space 4} .2452276{col 98}{space 3} .7220341
{txt}{space 41}4  {c |}{col 45}{res}{space 2}  .477844{col 57}{space 2} .1187135{col 68}{space 1}   -2.97{col 77}{space 3}0.003{col 85}{space 4} .2935973{col 98}{space 3} .7777143
{txt}{space 30}Top Quintile  {c |}{col 45}{res}{space 2}  .611061{col 57}{space 2} .1460378{col 68}{space 1}   -2.06{col 77}{space 3}0.039{col 85}{space 4}  .382466{col 98}{space 3} .9762843
{txt}{space 43} {c |}
{space 29}isco08_10group {c |}
{space 29}Professionals  {c |}{col 45}{res}{space 2}   .84713{col 57}{space 2} .1682245{col 68}{space 1}   -0.84{col 77}{space 3}0.404{col 85}{space 4} .5739382{col 98}{space 3}  1.25036
{txt}{space 3}Technicians and Associate Professionals  {c |}{col 45}{res}{space 2} .8018153{col 57}{space 2} .2226908{col 68}{space 1}   -0.80{col 77}{space 3}0.426{col 85}{space 4} .4651517{col 98}{space 3} 1.382146
{txt}{space 18}Clerical Support Workers  {c |}{col 45}{res}{space 2} .4197725{col 57}{space 2} .1390119{col 68}{space 1}   -2.62{col 77}{space 3}0.009{col 85}{space 4} .2193023{col 98}{space 3} .8034978
{txt}{space 17}Service and Sales Workers  {c |}{col 45}{res}{space 2} .6119555{col 57}{space 2} .2368697{col 68}{space 1}   -1.27{col 77}{space 3}0.205{col 85}{space 4} .2865126{col 98}{space 3} 1.307061
{txt}{space 14}Skilled Agricultural Workers  {c |}{col 45}{res}{space 2}        1{col 57}{txt}  (empty)
{space 10}Craft and Related Trades Workers  {c |}{col 45}{res}{space 2} .6431185{col 57}{space 2} .3194756{col 68}{space 1}   -0.89{col 77}{space 3}0.374{col 85}{space 4} .2428388{col 98}{space 3} 1.703194
{txt}Plant and Machine Operators and Assemblers  {c |}{col 45}{res}{space 2} .9532686{col 57}{space 2} .5881398{col 68}{space 1}   -0.08{col 77}{space 3}0.938{col 85}{space 4} .2843718{col 98}{space 3} 3.195539
{txt}{space 20}Elementary Occupations  {c |}{col 45}{res}{space 2} .8409134{col 57}{space 2} .4688251{col 68}{space 1}   -0.31{col 77}{space 3}0.756{col 85}{space 4} .2818671{col 98}{space 3} 2.508755
{txt}{space 43} {c |}
{space 30}std_RTI_Autor {c |}{col 45}{res}{space 2} .9940924{col 57}{space 2} .1978822{col 68}{space 1}   -0.03{col 77}{space 3}0.976{col 85}{space 4} .6728775{col 98}{space 3} 1.468647
{txt}{space 16}std_offshorable_Blinder2007 {c |}{col 45}{res}{space 2} 1.074188{col 57}{space 2} .0791599{col 68}{space 1}    0.97{col 77}{space 3}0.332{col 85}{space 4}   .92968{col 98}{space 3} 1.241158
{txt}{space 38}_cons {c |}{col 45}{res}{space 2} .2363139{col 57}{space 2} .1024446{col 68}{space 1}   -3.33{col 77}{space 3}0.001{col 85}{space 4} .1010115{col 98}{space 3} .5528507
{txt}{hline 44}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. ** Model 3a
. svy: logit AI_directionworse aiexposure_felten2021 if infullmodel_TableC1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}   1{txt},{res}   1321{txt}){col 67}= {res}      7.20
{txt}{col 49}Prob > F{col 67}= {res}    0.0074

{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}  Linearized
{col 1}    AI_directionworse{col 23}{c |} Odds Ratio{col 35}   Std. Err.{col 47}      t{col 55}   P>|t|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
aiexposure_felten2021 {c |}{col 23}{res}{space 2} .7601341{col 35}{space 2} .0776837{col 46}{space 1}   -2.68{col 55}{space 3}0.007{col 63}{space 4} .6220423{col 76}{space 3} .9288821
{txt}{space 16}_cons {c |}{col 23}{res}{space 2} 3.104447{col 35}{space 2} .3424206{col 46}{space 1}   10.27{col 55}{space 3}0.000{col 63}{space 4} 2.500406{col 76}{space 3}  3.85441
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 3b
. svy: logit AI_directionworse aiexposure_felten2021 i.isco08_10group if infullmodel_TableC1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}note: 6.isco08_10group != 0 predicts success perfectly
      6.isco08_10group dropped and 2 obs not used

Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,320
{txt}{col 1}Number of PSUs{col 20}= {res}    1,320{txt}{col 49}Population size{col 67}={res} 1,347.7684
{txt}{col 49}Design df{col 67}= {res}     1,319
{txt}{col 49}F({res}   8{txt},{res}   1312{txt}){col 67}= {res}      5.13
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 44}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 45}{c |}{col 57}  Linearized
{col 1}                          AI_directionworse{col 45}{c |} Odds Ratio{col 57}   Std. Err.{col 69}      t{col 77}   P>|t|{col 85}     [95% Con{col 98}f. Interval]
{hline 44}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}aiexposure_felten2021 {c |}{col 45}{res}{space 2} .7822377{col 57}{space 2} .1143868{col 68}{space 1}   -1.68{col 77}{space 3}0.093{col 85}{space 4} .5871553{col 98}{space 3} 1.042136
{txt}{space 43} {c |}
{space 29}isco08_10group {c |}
{space 29}Professionals  {c |}{col 45}{res}{space 2} 1.403505{col 57}{space 2} .3203995{col 68}{space 1}    1.48{col 77}{space 3}0.138{col 85}{space 4} .8968477{col 98}{space 3} 2.196388
{txt}{space 3}Technicians and Associate Professionals  {c |}{col 45}{res}{space 2} 1.645451{col 57}{space 2} .4233985{col 68}{space 1}    1.94{col 77}{space 3}0.053{col 85}{space 4} .9932468{col 98}{space 3} 2.725917
{txt}{space 18}Clerical Support Workers  {c |}{col 45}{res}{space 2}  4.38467{col 57}{space 2} 1.300394{col 68}{space 1}    4.98{col 77}{space 3}0.000{col 85}{space 4} 2.450518{col 98}{space 3} 7.845416
{txt}{space 17}Service and Sales Workers  {c |}{col 45}{res}{space 2}  2.08287{col 57}{space 2} .8153354{col 68}{space 1}    1.87{col 77}{space 3}0.061{col 85}{space 4} .9663953{col 98}{space 3} 4.489204
{txt}{space 14}Skilled Agricultural Workers  {c |}{col 45}{res}{space 2}        1{col 57}{txt}  (empty)
{space 10}Craft and Related Trades Workers  {c |}{col 45}{res}{space 2} 1.761217{col 57}{space 2}  .950899{col 68}{space 1}    1.05{col 77}{space 3}0.295{col 85}{space 4} .6106867{col 98}{space 3} 5.079341
{txt}Plant and Machine Operators and Assemblers  {c |}{col 45}{res}{space 2} 1.237565{col 57}{space 2} .8057694{col 68}{space 1}    0.33{col 77}{space 3}0.743{col 85}{space 4} .3450229{col 98}{space 3}  4.43903
{txt}{space 20}Elementary Occupations  {c |}{col 45}{res}{space 2} 1.623214{col 57}{space 2} .9168243{col 68}{space 1}    0.86{col 77}{space 3}0.391{col 85}{space 4} .5359917{col 98}{space 3}  4.91579
{txt}{space 43} {c |}
{space 38}_cons {c |}{col 45}{res}{space 2} 1.756842{col 57}{space 2} .4537982{col 68}{space 1}    2.18{col 77}{space 3}0.029{col 85}{space 4} 1.058431{col 98}{space 3} 2.916104
{txt}{hline 44}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 3c
. svy: logit AI_directionworse aiexposure_felten2021 $controls_demographics $controls_occupindices if infullmodel_TableC1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}note: 6.isco08_10group != 0 predicts success perfectly
      6.isco08_10group dropped and 2 obs not used

Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,320
{txt}{col 1}Number of PSUs{col 20}= {res}    1,320{txt}{col 49}Population size{col 67}={res} 1,347.7684
{txt}{col 49}Design df{col 67}= {res}     1,319
{txt}{col 49}F({res}  22{txt},{res}   1298{txt}){col 67}= {res}      4.36
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 44}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 45}{c |}{col 57}  Linearized
{col 1}                          AI_directionworse{col 45}{c |} Odds Ratio{col 57}   Std. Err.{col 69}      t{col 77}   P>|t|{col 85}     [95% Con{col 98}f. Interval]
{hline 44}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}aiexposure_felten2021 {c |}{col 45}{res}{space 2} .8255058{col 57}{space 2} .1279553{col 68}{space 1}   -1.24{col 77}{space 3}0.216{col 85}{space 4} .6090609{col 98}{space 3}  1.11887
{txt}{space 40}age {c |}{col 45}{res}{space 2} 1.022576{col 57}{space 2} .0064969{col 68}{space 1}    3.51{col 77}{space 3}0.000{col 85}{space 4}  1.00991{col 98}{space 3} 1.035401
{txt}{space 43} {c |}
{space 31}female_dummy {c |}
{space 38}Male  {c |}{col 45}{res}{space 2} .7398068{col 57}{space 2} .1084868{col 68}{space 1}   -2.06{col 77}{space 3}0.040{col 85}{space 4} .5548578{col 98}{space 3} .9864042
{txt}{space 43} {c |}
{space 31}degree_dummy {c |}
{space 29}Degree-Holder  {c |}{col 45}{res}{space 2} .7578055{col 57}{space 2} .1174403{col 68}{space 1}   -1.79{col 77}{space 3}0.074{col 85}{space 4} .5591423{col 98}{space 3} 1.027054
{txt}{space 43} {c |}
{space 26}employmentstatus5 {c |}
{space 33}FT Worker  {c |}{col 45}{res}{space 2} .4608474{col 57}{space 2} .1068904{col 68}{space 1}   -3.34{col 77}{space 3}0.001{col 85}{space 4} .2923786{col 98}{space 3} .7263883
{txt}{space 43} {c |}
{space 30}employsector5 {c |}
{space 20}Public Sector Employee  {c |}{col 45}{res}{space 2} 1.227548{col 57}{space 2} .2181767{col 68}{space 1}    1.15{col 77}{space 3}0.249{col 85}{space 4}  .866188{col 98}{space 3} 1.739661
{txt}{space 7}Charity / Voluntary Sector Employee  {c |}{col 45}{res}{space 2} 1.251154{col 57}{space 2} .4121188{col 68}{space 1}    0.68{col 77}{space 3}0.496{col 85}{space 4} .6556546{col 98}{space 3} 2.387516
{txt}{space 13}Self-Employed / Company Owner  {c |}{col 45}{res}{space 2} 1.021456{col 57}{space 2} .2820794{col 68}{space 1}    0.08{col 77}{space 3}0.939{col 85}{space 4} .5942113{col 98}{space 3} 1.755895
{txt}{space 32}Other / DK  {c |}{col 45}{res}{space 2} .6713104{col 57}{space 2} .3897602{col 68}{space 1}   -0.69{col 77}{space 3}0.493{col 85}{space 4} .2149143{col 98}{space 3} 2.096918
{txt}{space 43} {c |}
{space 13}equivincomeHHquintile_HBAI_imp {c |}
{space 41}2  {c |}{col 45}{res}{space 2} 1.836615{col 57}{space 2} .6268098{col 68}{space 1}    1.78{col 77}{space 3}0.075{col 85}{space 4} .9402617{col 98}{space 3} 3.587464
{txt}{space 41}3  {c |}{col 45}{res}{space 2}  2.18074{col 57}{space 2} .6864236{col 68}{space 1}    2.48{col 77}{space 3}0.013{col 85}{space 4} 1.176056{col 98}{space 3} 4.043706
{txt}{space 41}4  {c |}{col 45}{res}{space 2} 1.905224{col 57}{space 2} .5469452{col 68}{space 1}    2.25{col 77}{space 3}0.025{col 85}{space 4} 1.084829{col 98}{space 3} 3.346035
{txt}{space 30}Top Quintile  {c |}{col 45}{res}{space 2} 1.493235{col 57}{space 2}  .415525{col 68}{space 1}    1.44{col 77}{space 3}0.150{col 85}{space 4} .8650585{col 98}{space 3} 2.577573
{txt}{space 43} {c |}
{space 29}isco08_10group {c |}
{space 29}Professionals  {c |}{col 45}{res}{space 2} 1.352457{col 57}{space 2} .3266098{col 68}{space 1}    1.25{col 77}{space 3}0.211{col 85}{space 4} .8421221{col 98}{space 3} 2.172061
{txt}{space 3}Technicians and Associate Professionals  {c |}{col 45}{res}{space 2} 1.129764{col 57}{space 2} .3862974{col 68}{space 1}    0.36{col 77}{space 3}0.721{col 85}{space 4} .5776586{col 98}{space 3} 2.209552
{txt}{space 18}Clerical Support Workers  {c |}{col 45}{res}{space 2} 2.358452{col 57}{space 2} .9137947{col 68}{space 1}    2.21{col 77}{space 3}0.027{col 85}{space 4} 1.102863{col 98}{space 3} 5.043505
{txt}{space 17}Service and Sales Workers  {c |}{col 45}{res}{space 2} 1.486326{col 57}{space 2} .6631041{col 68}{space 1}    0.89{col 77}{space 3}0.375{col 85}{space 4} .6194597{col 98}{space 3} 3.566275
{txt}{space 14}Skilled Agricultural Workers  {c |}{col 45}{res}{space 2}        1{col 57}{txt}  (empty)
{space 10}Craft and Related Trades Workers  {c |}{col 45}{res}{space 2} 1.493258{col 57}{space 2} .8999396{col 68}{space 1}    0.67{col 77}{space 3}0.506{col 85}{space 4} .4577956{col 98}{space 3} 4.870778
{txt}Plant and Machine Operators and Assemblers  {c |}{col 45}{res}{space 2} .9329411{col 57}{space 2} .6460808{col 68}{space 1}   -0.10{col 77}{space 3}0.920{col 85}{space 4}  .239794{col 98}{space 3} 3.629695
{txt}{space 20}Elementary Occupations  {c |}{col 45}{res}{space 2} .8801922{col 57}{space 2} .5969058{col 68}{space 1}   -0.19{col 77}{space 3}0.851{col 85}{space 4} .2327028{col 98}{space 3} 3.329303
{txt}{space 43} {c |}
{space 30}std_RTI_Autor {c |}{col 45}{res}{space 2} 1.401743{col 57}{space 2} .3410149{col 68}{space 1}    1.39{col 77}{space 3}0.165{col 85}{space 4} .8697587{col 98}{space 3} 2.259115
{txt}{space 16}std_offshorable_Blinder2007 {c |}{col 45}{res}{space 2} .9578306{col 57}{space 2} .0813943{col 68}{space 1}   -0.51{col 77}{space 3}0.612{col 85}{space 4} .8107539{col 98}{space 3} 1.131588
{txt}{space 38}_cons {c |}{col 45}{res}{space 2} 1.358433{col 57}{space 2} .7005061{col 68}{space 1}    0.59{col 77}{space 3}0.553{col 85}{space 4} .4939623{col 98}{space 3} 3.735789
{txt}{hline 44}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. 
. ******* TABLE C2 ******
. 
. ** Model 1a
. svy: logit AI_worsen i.aiexposure_pizzinelli2023 if infullmodel_TableC1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}   2{txt},{res}   3960{txt}){col 67}= {res}     26.05
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 33}{c |}{col 45}  Linearized
{col 1}                      AI_worsen{col 33}{c |} Odds Ratio{col 45}   Std. Err.{col 57}      t{col 65}   P>|t|{col 73}     [95% Con{col 86}f. Interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}aiexposure_pizzinelli2023 {c |}
{space 1}High Exposure, Low Compliment  {c |}{col 33}{res}{space 2} 2.253628{col 45}{space 2} .2685664{col 56}{space 1}    6.82{col 65}{space 3}0.000{col 73}{space 4} 1.784075{col 86}{space 3} 2.846765
{txt}High Exposure, High Compliment  {c |}{col 33}{res}{space 2} 1.541842{col 45}{space 2} .2002989{col 56}{space 1}    3.33{col 65}{space 3}0.001{col 73}{space 4} 1.195164{col 86}{space 3} 1.989081
{txt}{space 31} {c |}
{space 26}_cons {c |}{col 33}{res}{space 2} .1812414{col 45}{space 2} .0192322{col 56}{space 1}  -16.10{col 65}{space 3}0.000{col 73}{space 4} .1471994{col 86}{space 3} .2231563
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 1b
. svy: logit AI_worsen i.aiexposure_pizzinelli2023 i.isco08_10group if infullmodel_TableC1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}  10{txt},{res}   3952{txt}){col 67}= {res}      7.33
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 44}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 45}{c |}{col 57}  Linearized
{col 1}                                  AI_worsen{col 45}{c |} Odds Ratio{col 57}   Std. Err.{col 69}      t{col 77}   P>|t|{col 85}     [95% Con{col 98}f. Interval]
{hline 44}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 18}aiexposure_pizzinelli2023 {c |}
{space 13}High Exposure, Low Compliment  {c |}{col 45}{res}{space 2}  1.74139{col 57}{space 2} .3043455{col 68}{space 1}    3.17{col 77}{space 3}0.002{col 85}{space 4} 1.236189{col 98}{space 3} 2.453055
{txt}{space 12}High Exposure, High Compliment  {c |}{col 45}{res}{space 2} 1.276769{col 57}{space 2} .2282831{col 68}{space 1}    1.37{col 77}{space 3}0.172{col 85}{space 4} .8992344{col 98}{space 3} 1.812807
{txt}{space 43} {c |}
{space 29}isco08_10group {c |}
{space 29}Professionals  {c |}{col 45}{res}{space 2} 1.337072{col 57}{space 2} .2249748{col 68}{space 1}    1.73{col 77}{space 3}0.084{col 85}{space 4} .9613657{col 98}{space 3} 1.859607
{txt}{space 3}Technicians and Associate Professionals  {c |}{col 45}{res}{space 2} 1.184741{col 57}{space 2} .2183317{col 68}{space 1}    0.92{col 77}{space 3}0.358{col 85}{space 4}  .825488{col 98}{space 3} 1.700342
{txt}{space 18}Clerical Support Workers  {c |}{col 45}{res}{space 2} 1.499598{col 57}{space 2} .2859038{col 68}{space 1}    2.13{col 77}{space 3}0.034{col 85}{space 4} 1.031907{col 98}{space 3}  2.17926
{txt}{space 17}Service and Sales Workers  {c |}{col 45}{res}{space 2} .8199835{col 57}{space 2} .1671243{col 68}{space 1}   -0.97{col 77}{space 3}0.330{col 85}{space 4} .5498763{col 98}{space 3} 1.222771
{txt}{space 14}Skilled Agricultural Workers  {c |}{col 45}{res}{space 2} .2956963{col 57}{space 2} .2379506{col 68}{space 1}   -1.51{col 77}{space 3}0.130{col 85}{space 4} .0610469{col 98}{space 3}  1.43228
{txt}{space 10}Craft and Related Trades Workers  {c |}{col 45}{res}{space 2} .9885009{col 57}{space 2} .3375098{col 68}{space 1}   -0.03{col 77}{space 3}0.973{col 85}{space 4} .5061246{col 98}{space 3}  1.93062
{txt}Plant and Machine Operators and Assemblers  {c |}{col 45}{res}{space 2} 1.003525{col 57}{space 2} .3581733{col 68}{space 1}    0.01{col 77}{space 3}0.992{col 85}{space 4} .4984582{col 98}{space 3} 2.020356
{txt}{space 20}Elementary Occupations  {c |}{col 45}{res}{space 2} 1.073032{col 57}{space 2} .3123419{col 68}{space 1}    0.24{col 77}{space 3}0.809{col 85}{space 4} .6064097{col 98}{space 3} 1.898712
{txt}{space 43} {c |}
{space 38}_cons {c |}{col 45}{res}{space 2} .1851487{col 57}{space 2} .0408001{col 68}{space 1}   -7.65{col 77}{space 3}0.000{col 85}{space 4} .1201958{col 98}{space 3} .2852016
{txt}{hline 44}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 1c
. svy: logit AI_worsen i.aiexposure_pizzinelli2023 $controls_demographics $controls_occupindices if infullmodel_TableC1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}  24{txt},{res}   3938{txt}){col 67}= {res}      4.58
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 44}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 45}{c |}{col 57}  Linearized
{col 1}                                  AI_worsen{col 45}{c |} Odds Ratio{col 57}   Std. Err.{col 69}      t{col 77}   P>|t|{col 85}     [95% Con{col 98}f. Interval]
{hline 44}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 18}aiexposure_pizzinelli2023 {c |}
{space 13}High Exposure, Low Compliment  {c |}{col 45}{res}{space 2}  1.64133{col 57}{space 2} .2968011{col 68}{space 1}    2.74{col 77}{space 3}0.006{col 85}{space 4} 1.151401{col 98}{space 3} 2.339729
{txt}{space 12}High Exposure, High Compliment  {c |}{col 45}{res}{space 2} 1.447752{col 57}{space 2} .2709196{col 68}{space 1}    1.98{col 77}{space 3}0.048{col 85}{space 4} 1.003134{col 98}{space 3} 2.089436
{txt}{space 43} {c |}
{space 40}age {c |}{col 45}{res}{space 2} .9933056{col 57}{space 2} .0036289{col 68}{space 1}   -1.84{col 77}{space 3}0.066{col 85}{space 4} .9862163{col 98}{space 3} 1.000446
{txt}{space 43} {c |}
{space 31}female_dummy {c |}
{space 38}Male  {c |}{col 45}{res}{space 2} 1.131844{col 57}{space 2} .1015309{col 68}{space 1}    1.38{col 77}{space 3}0.167{col 85}{space 4} .9493083{col 98}{space 3} 1.349479
{txt}{space 43} {c |}
{space 31}degree_dummy {c |}
{space 29}Degree-Holder  {c |}{col 45}{res}{space 2} 1.267243{col 57}{space 2} .1171289{col 68}{space 1}    2.56{col 77}{space 3}0.010{col 85}{space 4} 1.057209{col 98}{space 3} 1.519004
{txt}{space 43} {c |}
{space 26}employmentstatus5 {c |}
{space 33}FT Worker  {c |}{col 45}{res}{space 2} .7991472{col 57}{space 2} .0853095{col 68}{space 1}   -2.10{col 77}{space 3}0.036{col 85}{space 4} .6482352{col 98}{space 3} .9851921
{txt}{space 43} {c |}
{space 30}employsector5 {c |}
{space 20}Public Sector Employee  {c |}{col 45}{res}{space 2} 1.025641{col 57}{space 2} .1023862{col 68}{space 1}    0.25{col 77}{space 3}0.800{col 85}{space 4} .8433287{col 98}{space 3} 1.247366
{txt}{space 7}Charity / Voluntary Sector Employee  {c |}{col 45}{res}{space 2} .9735079{col 57}{space 2} .1760185{col 68}{space 1}   -0.15{col 77}{space 3}0.882{col 85}{space 4} .6829485{col 98}{space 3} 1.387685
{txt}{space 13}Self-Employed / Company Owner  {c |}{col 45}{res}{space 2} 1.107717{col 57}{space 2} .1622806{col 68}{space 1}    0.70{col 77}{space 3}0.485{col 85}{space 4} .8311697{col 98}{space 3} 1.476278
{txt}{space 32}Other / DK  {c |}{col 45}{res}{space 2} .8433342{col 57}{space 2} .2374032{col 68}{space 1}   -0.61{col 77}{space 3}0.545{col 85}{space 4} .4856335{col 98}{space 3} 1.464505
{txt}{space 43} {c |}
{space 13}equivincomeHHquintile_HBAI_imp {c |}
{space 41}2  {c |}{col 45}{res}{space 2} 1.096418{col 57}{space 2} .1846493{col 68}{space 1}    0.55{col 77}{space 3}0.585{col 85}{space 4} .7880983{col 98}{space 3} 1.525359
{txt}{space 41}3  {c |}{col 45}{res}{space 2} 1.016173{col 57}{space 2}  .165523{col 68}{space 1}    0.10{col 77}{space 3}0.922{col 85}{space 4} .7383701{col 98}{space 3} 1.398495
{txt}{space 41}4  {c |}{col 45}{res}{space 2} 1.028249{col 57}{space 2} .1646957{col 68}{space 1}    0.17{col 77}{space 3}0.862{col 85}{space 4} .7511363{col 98}{space 3} 1.407595
{txt}{space 30}Top Quintile  {c |}{col 45}{res}{space 2} 1.062649{col 57}{space 2} .1716405{col 68}{space 1}    0.38{col 77}{space 3}0.707{col 85}{space 4} .7742134{col 98}{space 3} 1.458543
{txt}{space 43} {c |}
{space 29}isco08_10group {c |}
{space 29}Professionals  {c |}{col 45}{res}{space 2} 1.224066{col 57}{space 2}  .209545{col 68}{space 1}    1.18{col 77}{space 3}0.238{col 85}{space 4} .8750747{col 98}{space 3}  1.71224
{txt}{space 3}Technicians and Associate Professionals  {c |}{col 45}{res}{space 2} .8932767{col 57}{space 2} .1928682{col 68}{space 1}   -0.52{col 77}{space 3}0.601{col 85}{space 4} .5849869{col 98}{space 3} 1.364036
{txt}{space 18}Clerical Support Workers  {c |}{col 45}{res}{space 2} 1.097648{col 57}{space 2} .2500703{col 68}{space 1}    0.41{col 77}{space 3}0.683{col 85}{space 4} .7022307{col 98}{space 3} 1.715719
{txt}{space 17}Service and Sales Workers  {c |}{col 45}{res}{space 2} .8030406{col 57}{space 2} .1811776{col 68}{space 1}   -0.97{col 77}{space 3}0.331{col 85}{space 4}  .515983{col 98}{space 3} 1.249797
{txt}{space 14}Skilled Agricultural Workers  {c |}{col 45}{res}{space 2} .2530618{col 57}{space 2} .2076035{col 68}{space 1}   -1.68{col 77}{space 3}0.094{col 85}{space 4} .0506659{col 98}{space 3} 1.263972
{txt}{space 10}Craft and Related Trades Workers  {c |}{col 45}{res}{space 2} .8574314{col 57}{space 2} .3131619{col 68}{space 1}   -0.42{col 77}{space 3}0.674{col 85}{space 4} .4190036{col 98}{space 3} 1.754611
{txt}Plant and Machine Operators and Assemblers  {c |}{col 45}{res}{space 2} .8018312{col 57}{space 2} .3029206{col 68}{space 1}   -0.58{col 77}{space 3}0.559{col 85}{space 4} .3823074{col 98}{space 3} 1.681718
{txt}{space 20}Elementary Occupations  {c |}{col 45}{res}{space 2} .7172723{col 57}{space 2}  .236531{col 68}{space 1}   -1.01{col 77}{space 3}0.314{col 85}{space 4} .3757526{col 98}{space 3} 1.369197
{txt}{space 43} {c |}
{space 30}std_RTI_Autor {c |}{col 45}{res}{space 2} 1.544808{col 57}{space 2}   .19088{col 68}{space 1}    3.52{col 77}{space 3}0.000{col 85}{space 4} 1.212456{col 98}{space 3} 1.968262
{txt}{space 16}std_offshorable_Blinder2007 {c |}{col 45}{res}{space 2} 1.049087{col 57}{space 2} .0541717{col 68}{space 1}    0.93{col 77}{space 3}0.353{col 85}{space 4} .9480794{col 98}{space 3} 1.160857
{txt}{space 38}_cons {c |}{col 45}{res}{space 2} .2781628{col 57}{space 2} .0905245{col 68}{space 1}   -3.93{col 77}{space 3}0.000{col 85}{space 4}  .146961{col 98}{space 3} .5264974
{txt}{hline 44}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. ** Model 2a
. svy: logit AI_improve i.aiexposure_pizzinelli2023 if infullmodel_TableC1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}   2{txt},{res}   3960{txt}){col 67}= {res}     15.52
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 33}{c |}{col 45}  Linearized
{col 1}                     AI_improve{col 33}{c |} Odds Ratio{col 45}   Std. Err.{col 57}      t{col 65}   P>|t|{col 73}     [95% Con{col 86}f. Interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}aiexposure_pizzinelli2023 {c |}
{space 1}High Exposure, Low Compliment  {c |}{col 33}{res}{space 2} 2.531605{col 45}{space 2} .5272212{col 56}{space 1}    4.46{col 65}{space 3}0.000{col 73}{space 4} 1.682961{col 86}{space 3} 3.808182
{txt}High Exposure, High Compliment  {c |}{col 33}{res}{space 2}  3.25763{col 45}{space 2} .6905999{col 56}{space 1}    5.57{col 65}{space 3}0.000{col 73}{space 4} 2.149792{col 86}{space 3} 4.936363
{txt}{space 31} {c |}
{space 26}_cons {c |}{col 33}{res}{space 2}  .044145{col 45}{space 2} .0083744{col 56}{space 1}  -16.45{col 65}{space 3}0.000{col 73}{space 4} .0304339{col 86}{space 3} .0640332
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 2b
. svy: logit AI_improve i.aiexposure_pizzinelli2023 i.isco08_10group if infullmodel_TableC1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}note: 6.isco08_10group != 0 predicts failure perfectly
      6.isco08_10group dropped and 25 obs not used

Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,937
{txt}{col 1}Number of PSUs{col 20}= {res}    3,937{txt}{col 49}Population size{col 67}={res} 4,053.6686
{txt}{col 49}Design df{col 67}= {res}     3,936
{txt}{col 49}F({res}   9{txt},{res}   3928{txt}){col 67}= {res}      7.48
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 44}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 45}{c |}{col 57}  Linearized
{col 1}                                 AI_improve{col 45}{c |} Odds Ratio{col 57}   Std. Err.{col 69}      t{col 77}   P>|t|{col 85}     [95% Con{col 98}f. Interval]
{hline 44}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 18}aiexposure_pizzinelli2023 {c |}
{space 13}High Exposure, Low Compliment  {c |}{col 45}{res}{space 2} 2.051567{col 57}{space 2} .5916131{col 68}{space 1}    2.49{col 77}{space 3}0.013{col 85}{space 4} 1.165596{col 98}{space 3} 3.610965
{txt}{space 12}High Exposure, High Compliment  {c |}{col 45}{res}{space 2} 1.920044{col 57}{space 2} .5537128{col 68}{space 1}    2.26{col 77}{space 3}0.024{col 85}{space 4} 1.090841{col 98}{space 3} 3.379565
{txt}{space 43} {c |}
{space 29}isco08_10group {c |}
{space 29}Professionals  {c |}{col 45}{res}{space 2} .9405495{col 57}{space 2} .1810706{col 68}{space 1}   -0.32{col 77}{space 3}0.750{col 85}{space 4} .6448542{col 98}{space 3} 1.371835
{txt}{space 3}Technicians and Associate Professionals  {c |}{col 45}{res}{space 2} .7182679{col 57}{space 2}  .169363{col 68}{space 1}   -1.40{col 77}{space 3}0.161{col 85}{space 4}  .452394{col 98}{space 3} 1.140397
{txt}{space 18}Clerical Support Workers  {c |}{col 45}{res}{space 2} .3089643{col 57}{space 2} .0893168{col 68}{space 1}   -4.06{col 77}{space 3}0.000{col 85}{space 4} .1752926{col 98}{space 3} .5445692
{txt}{space 17}Service and Sales Workers  {c |}{col 45}{res}{space 2} .3391467{col 57}{space 2} .1099756{col 68}{space 1}   -3.33{col 77}{space 3}0.001{col 85}{space 4} .1795901{col 98}{space 3} .6404612
{txt}{space 14}Skilled Agricultural Workers  {c |}{col 45}{res}{space 2}        1{col 57}{txt}  (empty)
{space 10}Craft and Related Trades Workers  {c |}{col 45}{res}{space 2} .5158719{col 57}{space 2} .2432762{col 68}{space 1}   -1.40{col 77}{space 3}0.161{col 85}{space 4} .2046476{col 98}{space 3} 1.300401
{txt}Plant and Machine Operators and Assemblers  {c |}{col 45}{res}{space 2} .6868499{col 57}{space 2}  .408679{col 68}{space 1}   -0.63{col 77}{space 3}0.528{col 85}{space 4}  .213913{col 98}{space 3} 2.205396
{txt}{space 20}Elementary Occupations  {c |}{col 45}{res}{space 2} .4878762{col 57}{space 2} .2369172{col 68}{space 1}   -1.48{col 77}{space 3}0.140{col 85}{space 4} .1882917{col 98}{space 3} 1.264119
{txt}{space 43} {c |}
{space 38}_cons {c |}{col 45}{res}{space 2} .0895238{col 57}{space 2} .0289268{col 68}{space 1}   -7.47{col 77}{space 3}0.000{col 85}{space 4} .0475133{col 98}{space 3} .1686794
{txt}{hline 44}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 2c
. svy: logit AI_improve i.aiexposure_pizzinelli2023 $controls_demographics $controls_occupindices if infullmodel_TableC1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}note: 6.isco08_10group != 0 predicts failure perfectly
      6.isco08_10group dropped and 25 obs not used

Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,937
{txt}{col 1}Number of PSUs{col 20}= {res}    3,937{txt}{col 49}Population size{col 67}={res} 4,053.6686
{txt}{col 49}Design df{col 67}= {res}     3,936
{txt}{col 49}F({res}  23{txt},{res}   3914{txt}){col 67}= {res}      8.13
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 44}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 45}{c |}{col 57}  Linearized
{col 1}                                 AI_improve{col 45}{c |} Odds Ratio{col 57}   Std. Err.{col 69}      t{col 77}   P>|t|{col 85}     [95% Con{col 98}f. Interval]
{hline 44}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 18}aiexposure_pizzinelli2023 {c |}
{space 13}High Exposure, Low Compliment  {c |}{col 45}{res}{space 2} 1.646875{col 57}{space 2} .5031582{col 68}{space 1}    1.63{col 77}{space 3}0.103{col 85}{space 4}   .90473{col 98}{space 3} 2.997797
{txt}{space 12}High Exposure, High Compliment  {c |}{col 45}{res}{space 2} 1.934639{col 57}{space 2} .5868784{col 68}{space 1}    2.18{col 77}{space 3}0.030{col 85}{space 4} 1.067348{col 98}{space 3} 3.506662
{txt}{space 43} {c |}
{space 40}age {c |}{col 45}{res}{space 2} .9689655{col 57}{space 2} .0056462{col 68}{space 1}   -5.41{col 77}{space 3}0.000{col 85}{space 4} .9579587{col 98}{space 3} .9800988
{txt}{space 43} {c |}
{space 31}female_dummy {c |}
{space 38}Male  {c |}{col 45}{res}{space 2}  1.62671{col 57}{space 2} .2154578{col 68}{space 1}    3.67{col 77}{space 3}0.000{col 85}{space 4} 1.254682{col 98}{space 3} 2.109048
{txt}{space 43} {c |}
{space 31}degree_dummy {c |}
{space 29}Degree-Holder  {c |}{col 45}{res}{space 2} 1.677693{col 57}{space 2} .2315625{col 68}{space 1}    3.75{col 77}{space 3}0.000{col 85}{space 4} 1.279941{col 98}{space 3}  2.19905
{txt}{space 43} {c |}
{space 26}employmentstatus5 {c |}
{space 33}FT Worker  {c |}{col 45}{res}{space 2} 2.006022{col 57}{space 2} .4156499{col 68}{space 1}    3.36{col 77}{space 3}0.001{col 85}{space 4} 1.336323{col 98}{space 3}  3.01134
{txt}{space 43} {c |}
{space 30}employsector5 {c |}
{space 20}Public Sector Employee  {c |}{col 45}{res}{space 2} .7742849{col 57}{space 2} .1196679{col 68}{space 1}   -1.66{col 77}{space 3}0.098{col 85}{space 4} .5718798{col 98}{space 3} 1.048327
{txt}{space 7}Charity / Voluntary Sector Employee  {c |}{col 45}{res}{space 2} .7168729{col 57}{space 2} .2058121{col 68}{space 1}   -1.16{col 77}{space 3}0.246{col 85}{space 4} .4083096{col 98}{space 3}  1.25862
{txt}{space 13}Self-Employed / Company Owner  {c |}{col 45}{res}{space 2} 1.225169{col 57}{space 2} .2815787{col 68}{space 1}    0.88{col 77}{space 3}0.377{col 85}{space 4} .7807383{col 98}{space 3} 1.922589
{txt}{space 32}Other / DK  {c |}{col 45}{res}{space 2} 1.219999{col 57}{space 2}  .641901{col 68}{space 1}    0.38{col 77}{space 3}0.705{col 85}{space 4} .4348732{col 98}{space 3} 3.422598
{txt}{space 43} {c |}
{space 13}equivincomeHHquintile_HBAI_imp {c |}
{space 41}2  {c |}{col 45}{res}{space 2}  .509436{col 57}{space 2} .1522053{col 68}{space 1}   -2.26{col 77}{space 3}0.024{col 85}{space 4} .2835934{col 98}{space 3} .9151307
{txt}{space 41}3  {c |}{col 45}{res}{space 2} .4284603{col 57}{space 2} .1188001{col 68}{space 1}   -3.06{col 77}{space 3}0.002{col 85}{space 4} .2487846{col 98}{space 3} .7379003
{txt}{space 41}4  {c |}{col 45}{res}{space 2} .4905006{col 57}{space 2} .1221689{col 68}{space 1}   -2.86{col 77}{space 3}0.004{col 85}{space 4} .3009995{col 98}{space 3} .7993066
{txt}{space 30}Top Quintile  {c |}{col 45}{res}{space 2} .6406074{col 57}{space 2}  .153448{col 68}{space 1}   -1.86{col 77}{space 3}0.063{col 85}{space 4} .4005313{col 98}{space 3} 1.024584
{txt}{space 43} {c |}
{space 29}isco08_10group {c |}
{space 29}Professionals  {c |}{col 45}{res}{space 2} .8913841{col 57}{space 2} .1780693{col 68}{space 1}   -0.58{col 77}{space 3}0.565{col 85}{space 4} .6025186{col 98}{space 3} 1.318741
{txt}{space 3}Technicians and Associate Professionals  {c |}{col 45}{res}{space 2} .8243831{col 57}{space 2} .2285856{col 68}{space 1}   -0.70{col 77}{space 3}0.486{col 85}{space 4} .4786683{col 98}{space 3} 1.419788
{txt}{space 18}Clerical Support Workers  {c |}{col 45}{res}{space 2} .4486908{col 57}{space 2} .1512474{col 68}{space 1}   -2.38{col 77}{space 3}0.017{col 85}{space 4} .2317025{col 98}{space 3} .8688877
{txt}{space 17}Service and Sales Workers  {c |}{col 45}{res}{space 2} .5594193{col 57}{space 2} .2063729{col 68}{space 1}   -1.57{col 77}{space 3}0.115{col 85}{space 4} .2714113{col 98}{space 3} 1.153047
{txt}{space 14}Skilled Agricultural Workers  {c |}{col 45}{res}{space 2}        1{col 57}{txt}  (empty)
{space 10}Craft and Related Trades Workers  {c |}{col 45}{res}{space 2} .6020888{col 57}{space 2} .2977263{col 68}{space 1}   -1.03{col 77}{space 3}0.305{col 85}{space 4} .2283606{col 98}{space 3} 1.587449
{txt}Plant and Machine Operators and Assemblers  {c |}{col 45}{res}{space 2} .8325018{col 57}{space 2} .5162063{col 68}{space 1}   -0.30{col 77}{space 3}0.768{col 85}{space 4} .2468434{col 98}{space 3} 2.807688
{txt}{space 20}Elementary Occupations  {c |}{col 45}{res}{space 2} .6432704{col 57}{space 2} .3436631{col 68}{space 1}   -0.83{col 77}{space 3}0.409{col 85}{space 4}  .225686{col 98}{space 3} 1.833507
{txt}{space 43} {c |}
{space 30}std_RTI_Autor {c |}{col 45}{res}{space 2}  1.02308{col 57}{space 2} .2068867{col 68}{space 1}    0.11{col 77}{space 3}0.910{col 85}{space 4}   .68822{col 98}{space 3}  1.52087
{txt}{space 16}std_offshorable_Blinder2007 {c |}{col 45}{res}{space 2} 1.121985{col 57}{space 2} .0836477{col 68}{space 1}    1.54{col 77}{space 3}0.123{col 85}{space 4} .9694105{col 98}{space 3} 1.298574
{txt}{space 38}_cons {c |}{col 45}{res}{space 2} .1907887{col 57}{space 2} .0944149{col 68}{space 1}   -3.35{col 77}{space 3}0.001{col 85}{space 4}  .072309{col 98}{space 3} .5033997
{txt}{hline 44}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. ** Model 3a
. svy: logit AI_directionworse ib3.aiexposure_pizzinelli2023 if infullmodel_TableC1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}   2{txt},{res}   1320{txt}){col 67}= {res}      8.12
{txt}{col 49}Prob > F{col 67}= {res}    0.0003

{txt}{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 32}{c |}{col 44}  Linearized
{col 1}             AI_directionworse{col 32}{c |} Odds Ratio{col 44}   Std. Err.{col 56}      t{col 64}   P>|t|{col 72}     [95% Con{col 85}f. Interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}aiexposure_pizzinelli2023 {c |}
{space 17}Low Exposure  {c |}{col 32}{res}{space 2} 2.089068{col 44}{space 2}  .495564{col 55}{space 1}    3.11{col 64}{space 3}0.002{col 72}{space 4} 1.311739{col 85}{space 3} 3.327039
{txt}High Exposure, Low Compliment  {c |}{col 32}{res}{space 2} 1.660678{col 44}{space 2} .2404057{col 55}{space 1}    3.50{col 64}{space 3}0.000{col 72}{space 4} 1.250113{col 85}{space 3} 2.206081
{txt}{space 30} {c |}
{space 25}_cons {c |}{col 32}{res}{space 2} 1.737184{col 44}{space 2} .1916768{col 55}{space 1}    5.01{col 64}{space 3}0.000{col 72}{space 4} 1.399072{col 85}{space 3} 2.157007
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 3b
. svy: logit AI_directionworse ib3.aiexposure_pizzinelli2023 i.isco08_10group if infullmodel_TableC1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}note: 6.isco08_10group != 0 predicts success perfectly
      6.isco08_10group dropped and 2 obs not used

Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,320
{txt}{col 1}Number of PSUs{col 20}= {res}    1,320{txt}{col 49}Population size{col 67}={res} 1,347.7684
{txt}{col 49}Design df{col 67}= {res}     1,319
{txt}{col 49}F({res}   9{txt},{res}   1311{txt}){col 67}= {res}      4.35
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 44}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 45}{c |}{col 57}  Linearized
{col 1}                          AI_directionworse{col 45}{c |} Odds Ratio{col 57}   Std. Err.{col 69}      t{col 77}   P>|t|{col 85}     [95% Con{col 98}f. Interval]
{hline 44}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 18}aiexposure_pizzinelli2023 {c |}
{space 30}Low Exposure  {c |}{col 45}{res}{space 2}  1.47507{col 57}{space 2} .4814705{col 68}{space 1}    1.19{col 77}{space 3}0.234{col 85}{space 4} .7775369{col 98}{space 3} 2.798366
{txt}{space 13}High Exposure, Low Compliment  {c |}{col 45}{res}{space 2}  1.15797{col 57}{space 2} .1972307{col 68}{space 1}    0.86{col 77}{space 3}0.389{col 85}{space 4} .8290554{col 98}{space 3} 1.617376
{txt}{space 43} {c |}
{space 29}isco08_10group {c |}
{space 29}Professionals  {c |}{col 45}{res}{space 2} 1.316382{col 57}{space 2} .3090259{col 68}{space 1}    1.17{col 77}{space 3}0.242{col 85}{space 4} .8305685{col 98}{space 3} 2.086356
{txt}{space 3}Technicians and Associate Professionals  {c |}{col 45}{res}{space 2}  1.52232{col 57}{space 2}  .429804{col 68}{space 1}    1.49{col 77}{space 3}0.137{col 85}{space 4} .8749061{col 98}{space 3} 2.648809
{txt}{space 18}Clerical Support Workers  {c |}{col 45}{res}{space 2} 3.950331{col 57}{space 2} 1.302304{col 68}{space 1}    4.17{col 77}{space 3}0.000{col 85}{space 4}    2.069{col 98}{space 3} 7.542348
{txt}{space 17}Service and Sales Workers  {c |}{col 45}{res}{space 2} 2.269555{col 57}{space 2} .8496469{col 68}{space 1}    2.19{col 77}{space 3}0.029{col 85}{space 4} 1.088895{col 98}{space 3} 4.730372
{txt}{space 14}Skilled Agricultural Workers  {c |}{col 45}{res}{space 2}        1{col 57}{txt}  (empty)
{space 10}Craft and Related Trades Workers  {c |}{col 45}{res}{space 2}  1.81537{col 57}{space 2} 1.007192{col 68}{space 1}    1.07{col 77}{space 3}0.283{col 85}{space 4} .6113269{col 98}{space 3} 5.390846
{txt}Plant and Machine Operators and Assemblers  {c |}{col 45}{res}{space 2} 1.380384{col 57}{space 2} .9240702{col 68}{space 1}    0.48{col 77}{space 3}0.630{col 85}{space 4} .3712417{col 98}{space 3} 5.132666
{txt}{space 20}Elementary Occupations  {c |}{col 45}{res}{space 2} 2.002488{col 57}{space 2}  1.10407{col 68}{space 1}    1.26{col 77}{space 3}0.208{col 85}{space 4} .6789375{col 98}{space 3} 5.906228
{txt}{space 43} {c |}
{space 38}_cons {c |}{col 45}{res}{space 2} 1.328433{col 57}{space 2} .2727514{col 68}{space 1}    1.38{col 77}{space 3}0.167{col 85}{space 4} .8879981{col 98}{space 3} 1.987318
{txt}{hline 44}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 3c
. svy: logit AI_directionworse ib3.aiexposure_pizzinelli2023 $controls_demographics $controls_occupindices if infullmodel_TableC1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}note: 6.isco08_10group != 0 predicts success perfectly
      6.isco08_10group dropped and 2 obs not used

Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,320
{txt}{col 1}Number of PSUs{col 20}= {res}    1,320{txt}{col 49}Population size{col 67}={res} 1,347.7684
{txt}{col 49}Design df{col 67}= {res}     1,319
{txt}{col 49}F({res}  23{txt},{res}   1297{txt}){col 67}= {res}      4.11
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 44}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 45}{c |}{col 57}  Linearized
{col 1}                          AI_directionworse{col 45}{c |} Odds Ratio{col 57}   Std. Err.{col 69}      t{col 77}   P>|t|{col 85}     [95% Con{col 98}f. Interval]
{hline 44}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 18}aiexposure_pizzinelli2023 {c |}
{space 30}Low Exposure  {c |}{col 45}{res}{space 2} 1.446636{col 57}{space 2} .4888681{col 68}{space 1}    1.09{col 77}{space 3}0.275{col 85}{space 4} .7454953{col 98}{space 3} 2.807203
{txt}{space 13}High Exposure, Low Compliment  {c |}{col 45}{res}{space 2} 1.200531{col 57}{space 2} .2330901{col 68}{space 1}    0.94{col 77}{space 3}0.347{col 85}{space 4} .8202682{col 98}{space 3} 1.757078
{txt}{space 43} {c |}
{space 40}age {c |}{col 45}{res}{space 2} 1.023163{col 57}{space 2} .0065165{col 68}{space 1}    3.60{col 77}{space 3}0.000{col 85}{space 4} 1.010459{col 98}{space 3} 1.036027
{txt}{space 43} {c |}
{space 31}female_dummy {c |}
{space 38}Male  {c |}{col 45}{res}{space 2} .7370457{col 57}{space 2} .1086765{col 68}{space 1}   -2.07{col 77}{space 3}0.039{col 85}{space 4} .5519128{col 98}{space 3} .9842793
{txt}{space 43} {c |}
{space 31}degree_dummy {c |}
{space 29}Degree-Holder  {c |}{col 45}{res}{space 2} .7591442{col 57}{space 2} .1170541{col 68}{space 1}   -1.79{col 77}{space 3}0.074{col 85}{space 4}   .56099{col 98}{space 3} 1.027291
{txt}{space 43} {c |}
{space 26}employmentstatus5 {c |}
{space 33}FT Worker  {c |}{col 45}{res}{space 2} .4696898{col 57}{space 2}  .108756{col 68}{space 1}   -3.26{col 77}{space 3}0.001{col 85}{space 4} .2982192{col 98}{space 3}  .739753
{txt}{space 43} {c |}
{space 30}employsector5 {c |}
{space 20}Public Sector Employee  {c |}{col 45}{res}{space 2} 1.249267{col 57}{space 2} .2221616{col 68}{space 1}    1.25{col 77}{space 3}0.211{col 85}{space 4}  .881341{col 98}{space 3} 1.770788
{txt}{space 7}Charity / Voluntary Sector Employee  {c |}{col 45}{res}{space 2} 1.286544{col 57}{space 2} .4246489{col 68}{space 1}    0.76{col 77}{space 3}0.445{col 85}{space 4} .6733038{col 98}{space 3}  2.45832
{txt}{space 13}Self-Employed / Company Owner  {c |}{col 45}{res}{space 2} 1.004537{col 57}{space 2} .2773525{col 68}{space 1}    0.02{col 77}{space 3}0.987{col 85}{space 4} .5844314{col 98}{space 3} 1.726627
{txt}{space 32}Other / DK  {c |}{col 45}{res}{space 2} .7015924{col 57}{space 2} .4115387{col 68}{space 1}   -0.60{col 77}{space 3}0.546{col 85}{space 4} .2219883{col 98}{space 3} 2.217378
{txt}{space 43} {c |}
{space 13}equivincomeHHquintile_HBAI_imp {c |}
{space 41}2  {c |}{col 45}{res}{space 2} 1.809001{col 57}{space 2} .6200678{col 68}{space 1}    1.73{col 77}{space 3}0.084{col 85}{space 4} .9234345{col 98}{space 3} 3.543819
{txt}{space 41}3  {c |}{col 45}{res}{space 2}  2.13493{col 57}{space 2} .6761627{col 68}{space 1}    2.39{col 77}{space 3}0.017{col 85}{space 4} 1.146961{col 98}{space 3} 3.973918
{txt}{space 41}4  {c |}{col 45}{res}{space 2} 1.901023{col 57}{space 2} .5476339{col 68}{space 1}    2.23{col 77}{space 3}0.026{col 85}{space 4} 1.080323{col 98}{space 3} 3.345191
{txt}{space 30}Top Quintile  {c |}{col 45}{res}{space 2} 1.463914{col 57}{space 2} .4094077{col 68}{space 1}    1.36{col 77}{space 3}0.173{col 85}{space 4} .8457545{col 98}{space 3} 2.533884
{txt}{space 43} {c |}
{space 29}isco08_10group {c |}
{space 29}Professionals  {c |}{col 45}{res}{space 2} 1.278651{col 57}{space 2} .3133093{col 68}{space 1}    1.00{col 77}{space 3}0.316{col 85}{space 4} .7906595{col 98}{space 3} 2.067828
{txt}{space 3}Technicians and Associate Professionals  {c |}{col 45}{res}{space 2} 1.053856{col 57}{space 2} .3661782{col 68}{space 1}    0.15{col 77}{space 3}0.880{col 85}{space 4} .5330241{col 98}{space 3} 2.083607
{txt}{space 18}Clerical Support Workers  {c |}{col 45}{res}{space 2} 2.172875{col 57}{space 2} .8625275{col 68}{space 1}    1.96{col 77}{space 3}0.051{col 85}{space 4}  .997328{col 98}{space 3} 4.734034
{txt}{space 17}Service and Sales Workers  {c |}{col 45}{res}{space 2} 1.485376{col 57}{space 2} .6421529{col 68}{space 1}    0.92{col 77}{space 3}0.360{col 85}{space 4} .6360774{col 98}{space 3} 3.468671
{txt}{space 14}Skilled Agricultural Workers  {c |}{col 45}{res}{space 2}        1{col 57}{txt}  (empty)
{space 10}Craft and Related Trades Workers  {c |}{col 45}{res}{space 2} 1.451375{col 57}{space 2} .8908755{col 68}{space 1}    0.61{col 77}{space 3}0.544{col 85}{space 4} .4353308{col 98}{space 3} 4.838822
{txt}Plant and Machine Operators and Assemblers  {c |}{col 45}{res}{space 2} .9784139{col 57}{space 2} .6804845{col 68}{space 1}   -0.03{col 77}{space 3}0.975{col 85}{space 4} .2500174{col 98}{space 3} 3.828909
{txt}{space 20}Elementary Occupations  {c |}{col 45}{res}{space 2} 1.010434{col 57}{space 2} .6493189{col 68}{space 1}    0.02{col 77}{space 3}0.987{col 85}{space 4} .2864254{col 98}{space 3} 3.564546
{txt}{space 43} {c |}
{space 30}std_RTI_Autor {c |}{col 45}{res}{space 2} 1.359072{col 57}{space 2} .3357915{col 68}{space 1}    1.24{col 77}{space 3}0.215{col 85}{space 4}  .837027{col 98}{space 3} 2.206711
{txt}{space 16}std_offshorable_Blinder2007 {c |}{col 45}{res}{space 2} .9274835{col 57}{space 2} .0805569{col 68}{space 1}   -0.87{col 77}{space 3}0.386{col 85}{space 4} .7821804{col 98}{space 3} 1.099779
{txt}{space 38}_cons {c |}{col 45}{res}{space 2} 1.031663{col 57}{space 2} .5133817{col 68}{space 1}    0.06{col 77}{space 3}0.950{col 85}{space 4} .3886595{col 98}{space 3}  2.73846
{txt}{hline 44}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. 
. ******* TABLE 1c ******
. 
. ** Model 1a
. svy: logit AI_worsen i.aiexposure_gmyrek2023 if infullmodel_TableC1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}   3{txt},{res}   3959{txt}){col 67}= {res}     16.83
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}  Linearized
{col 1}              AI_worsen{col 25}{c |} Odds Ratio{col 37}   Std. Err.{col 49}      t{col 57}   P>|t|{col 65}     [95% Con{col 78}f. Interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}aiexposure_gmyrek2023 {c |}
{space 2}Automation Potential  {c |}{col 25}{res}{space 2} 2.149197{col 37}{space 2} .2624887{col 48}{space 1}    6.26{col 57}{space 3}0.000{col 65}{space 4} 1.691548{col 78}{space 3} 2.730663
{txt}Augmentation Potential  {c |}{col 25}{res}{space 2} 1.014396{col 37}{space 2} .1403823{col 48}{space 1}    0.10{col 57}{space 3}0.918{col 65}{space 4}  .773346{col 78}{space 3} 1.330581
{txt}{space 11}Big Unknown  {c |}{col 25}{res}{space 2} 1.563634{col 37}{space 2} .1516716{col 48}{space 1}    4.61{col 57}{space 3}0.000{col 65}{space 4} 1.292837{col 78}{space 3} 1.891152
{txt}{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .2369267{col 37}{space 2} .0171015{col 48}{space 1}  -19.95{col 57}{space 3}0.000{col 65}{space 4} .2056625{col 78}{space 3} .2729437
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 1b
. svy: logit AI_worsen i.aiexposure_gmyrek2023 i.isco08_10group if infullmodel_TableC1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}  11{txt},{res}   3951{txt}){col 67}= {res}      6.76
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 44}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 45}{c |}{col 57}  Linearized
{col 1}                                  AI_worsen{col 45}{c |} Odds Ratio{col 57}   Std. Err.{col 69}      t{col 77}   P>|t|{col 85}     [95% Con{col 98}f. Interval]
{hline 44}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}aiexposure_gmyrek2023 {c |}
{space 22}Automation Potential  {c |}{col 45}{res}{space 2} 1.826461{col 57}{space 2} .3684252{col 68}{space 1}    2.99{col 77}{space 3}0.003{col 85}{space 4} 1.229865{col 98}{space 3}  2.71246
{txt}{space 20}Augmentation Potential  {c |}{col 45}{res}{space 2} .9305939{col 57}{space 2} .1315837{col 68}{space 1}   -0.51{col 77}{space 3}0.611{col 85}{space 4} .7052866{col 98}{space 3} 1.227877
{txt}{space 31}Big Unknown  {c |}{col 45}{res}{space 2} 1.345328{col 57}{space 2} .1554034{col 68}{space 1}    2.57{col 77}{space 3}0.010{col 85}{space 4} 1.072686{col 98}{space 3} 1.687265
{txt}{space 43} {c |}
{space 29}isco08_10group {c |}
{space 29}Professionals  {c |}{col 45}{res}{space 2}  1.37369{col 57}{space 2} .2302558{col 68}{space 1}    1.89{col 77}{space 3}0.058{col 85}{space 4} .9889357{col 98}{space 3} 1.908136
{txt}{space 3}Technicians and Associate Professionals  {c |}{col 45}{res}{space 2}  1.17467{col 57}{space 2} .2176106{col 68}{space 1}    0.87{col 77}{space 3}0.385{col 85}{space 4} .8169218{col 98}{space 3} 1.689084
{txt}{space 18}Clerical Support Workers  {c |}{col 45}{res}{space 2} 1.179589{col 57}{space 2} .2698905{col 68}{space 1}    0.72{col 77}{space 3}0.470{col 85}{space 4} .7532099{col 98}{space 3} 1.847335
{txt}{space 17}Service and Sales Workers  {c |}{col 45}{res}{space 2} .7702178{col 57}{space 2} .1496216{col 68}{space 1}   -1.34{col 77}{space 3}0.179{col 85}{space 4} .5262711{col 98}{space 3} 1.127243
{txt}{space 14}Skilled Agricultural Workers  {c |}{col 45}{res}{space 2} .2328443{col 57}{space 2} .1837878{col 68}{space 1}   -1.85{col 77}{space 3}0.065{col 85}{space 4} .0495442{col 98}{space 3} 1.094305
{txt}{space 10}Craft and Related Trades Workers  {c |}{col 45}{res}{space 2} .8686333{col 57}{space 2} .2780196{col 68}{space 1}   -0.44{col 77}{space 3}0.660{col 85}{space 4} .4637809{col 98}{space 3} 1.626897
{txt}Plant and Machine Operators and Assemblers  {c |}{col 45}{res}{space 2} .7991018{col 57}{space 2} .2546687{col 68}{space 1}   -0.70{col 77}{space 3}0.482{col 85}{space 4} .4278058{col 98}{space 3} 1.492649
{txt}{space 20}Elementary Occupations  {c |}{col 45}{res}{space 2} .8638258{col 57}{space 2} .2176889{col 68}{space 1}   -0.58{col 77}{space 3}0.561{col 85}{space 4} .5270515{col 98}{space 3} 1.415792
{txt}{space 43} {c |}
{space 38}_cons {c |}{col 45}{res}{space 2} .2351262{col 57}{space 2}  .036439{col 68}{space 1}   -9.34{col 77}{space 3}0.000{col 85}{space 4}  .173518{col 98}{space 3} .3186086
{txt}{hline 44}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 1c
. svy: logit AI_worsen i.aiexposure_gmyrek2023 $controls_demographics $controls_occupindices if infullmodel_TableC1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}  25{txt},{res}   3937{txt}){col 67}= {res}      4.61
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 44}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 45}{c |}{col 57}  Linearized
{col 1}                                  AI_worsen{col 45}{c |} Odds Ratio{col 57}   Std. Err.{col 69}      t{col 77}   P>|t|{col 85}     [95% Con{col 98}f. Interval]
{hline 44}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}aiexposure_gmyrek2023 {c |}
{space 22}Automation Potential  {c |}{col 45}{res}{space 2} 1.745978{col 57}{space 2} .3610829{col 68}{space 1}    2.69{col 77}{space 3}0.007{col 85}{space 4}  1.16399{col 98}{space 3} 2.618956
{txt}{space 20}Augmentation Potential  {c |}{col 45}{res}{space 2} .9600939{col 57}{space 2} .1382732{col 68}{space 1}   -0.28{col 77}{space 3}0.777{col 85}{space 4} .7239121{col 98}{space 3} 1.273332
{txt}{space 31}Big Unknown  {c |}{col 45}{res}{space 2} 1.307088{col 57}{space 2} .1556215{col 68}{space 1}    2.25{col 77}{space 3}0.025{col 85}{space 4} 1.034975{col 98}{space 3} 1.650743
{txt}{space 43} {c |}
{space 40}age {c |}{col 45}{res}{space 2} .9928332{col 57}{space 2}  .003648{col 68}{space 1}   -1.96{col 77}{space 3}0.050{col 85}{space 4} .9857068{col 98}{space 3} 1.000011
{txt}{space 43} {c |}
{space 31}female_dummy {c |}
{space 38}Male  {c |}{col 45}{res}{space 2} 1.136688{col 57}{space 2} .1019603{col 68}{space 1}    1.43{col 77}{space 3}0.153{col 85}{space 4} .9533791{col 98}{space 3} 1.355242
{txt}{space 43} {c |}
{space 31}degree_dummy {c |}
{space 29}Degree-Holder  {c |}{col 45}{res}{space 2} 1.277591{col 57}{space 2} .1179166{col 68}{space 1}    2.65{col 77}{space 3}0.008{col 85}{space 4} 1.066118{col 98}{space 3} 1.531011
{txt}{space 43} {c |}
{space 26}employmentstatus5 {c |}
{space 33}FT Worker  {c |}{col 45}{res}{space 2}  .785934{col 57}{space 2} .0838229{col 68}{space 1}   -2.26{col 77}{space 3}0.024{col 85}{space 4} .6376383{col 98}{space 3} .9687188
{txt}{space 43} {c |}
{space 30}employsector5 {c |}
{space 20}Public Sector Employee  {c |}{col 45}{res}{space 2} 1.065728{col 57}{space 2} .1070626{col 68}{space 1}    0.63{col 77}{space 3}0.526{col 85}{space 4} .8752029{col 98}{space 3} 1.297728
{txt}{space 7}Charity / Voluntary Sector Employee  {c |}{col 45}{res}{space 2}  .983229{col 57}{space 2} .1792324{col 68}{space 1}   -0.09{col 77}{space 3}0.926{col 85}{space 4} .6877682{col 98}{space 3} 1.405618
{txt}{space 13}Self-Employed / Company Owner  {c |}{col 45}{res}{space 2} 1.059116{col 57}{space 2} .1543521{col 68}{space 1}    0.39{col 77}{space 3}0.694{col 85}{space 4} .7958921{col 98}{space 3} 1.409395
{txt}{space 32}Other / DK  {c |}{col 45}{res}{space 2} .8401148{col 57}{space 2} .2386825{col 68}{space 1}   -0.61{col 77}{space 3}0.540{col 85}{space 4} .4813183{col 98}{space 3} 1.466375
{txt}{space 43} {c |}
{space 13}equivincomeHHquintile_HBAI_imp {c |}
{space 41}2  {c |}{col 45}{res}{space 2} 1.089056{col 57}{space 2} .1832577{col 68}{space 1}    0.51{col 77}{space 3}0.612{col 85}{space 4}   .78302{col 98}{space 3} 1.514703
{txt}{space 41}3  {c |}{col 45}{res}{space 2} 1.022176{col 57}{space 2} .1668668{col 68}{space 1}    0.13{col 77}{space 3}0.893{col 85}{space 4} .7422109{col 98}{space 3} 1.407745
{txt}{space 41}4  {c |}{col 45}{res}{space 2} 1.027057{col 57}{space 2} .1640473{col 68}{space 1}    0.17{col 77}{space 3}0.867{col 85}{space 4} .7509206{col 98}{space 3} 1.404736
{txt}{space 30}Top Quintile  {c |}{col 45}{res}{space 2} 1.066537{col 57}{space 2}  .172218{col 68}{space 1}    0.40{col 77}{space 3}0.690{col 85}{space 4} .7771177{col 98}{space 3} 1.463743
{txt}{space 43} {c |}
{space 29}isco08_10group {c |}
{space 29}Professionals  {c |}{col 45}{res}{space 2} 1.182521{col 57}{space 2} .2020125{col 68}{space 1}    0.98{col 77}{space 3}0.326{col 85}{space 4} .8459642{col 98}{space 3} 1.652973
{txt}{space 3}Technicians and Associate Professionals  {c |}{col 45}{res}{space 2} .8344136{col 57}{space 2} .1822707{col 68}{space 1}   -0.83{col 77}{space 3}0.407{col 85}{space 4} .5437342{col 98}{space 3}  1.28049
{txt}{space 18}Clerical Support Workers  {c |}{col 45}{res}{space 2}  .791225{col 57}{space 2} .2116085{col 68}{space 1}   -0.88{col 77}{space 3}0.381{col 85}{space 4} .4683619{col 98}{space 3} 1.336652
{txt}{space 17}Service and Sales Workers  {c |}{col 45}{res}{space 2} .7254118{col 57}{space 2}  .157901{col 68}{space 1}   -1.47{col 77}{space 3}0.140{col 85}{space 4} .4734195{col 98}{space 3} 1.111535
{txt}{space 14}Skilled Agricultural Workers  {c |}{col 45}{res}{space 2} .2020836{col 57}{space 2} .1635728{col 68}{space 1}   -1.98{col 77}{space 3}0.048{col 85}{space 4} .0413363{col 98}{space 3} .9879405
{txt}{space 10}Craft and Related Trades Workers  {c |}{col 45}{res}{space 2} .7379063{col 57}{space 2} .2610373{col 68}{space 1}   -0.86{col 77}{space 3}0.390{col 85}{space 4} .3688019{col 98}{space 3} 1.476418
{txt}Plant and Machine Operators and Assemblers  {c |}{col 45}{res}{space 2} .6314984{col 57}{space 2} .2241347{col 68}{space 1}   -1.30{col 77}{space 3}0.195{col 85}{space 4} .3148959{col 98}{space 3} 1.266419
{txt}{space 20}Elementary Occupations  {c |}{col 45}{res}{space 2} .5809856{col 57}{space 2} .1807313{col 68}{space 1}   -1.75{col 77}{space 3}0.081{col 85}{space 4} .3157149{col 98}{space 3} 1.069142
{txt}{space 43} {c |}
{space 30}std_RTI_Autor {c |}{col 45}{res}{space 2} 1.459191{col 57}{space 2} .1839698{col 68}{space 1}    3.00{col 77}{space 3}0.003{col 85}{space 4} 1.139627{col 98}{space 3} 1.868364
{txt}{space 16}std_offshorable_Blinder2007 {c |}{col 45}{res}{space 2} 1.092881{col 57}{space 2} .0557667{col 68}{space 1}    1.74{col 77}{space 3}0.082{col 85}{space 4} .9888384{col 98}{space 3} 1.207872
{txt}{space 38}_cons {c |}{col 45}{res}{space 2} .3829597{col 57}{space 2} .1102114{col 68}{space 1}   -3.34{col 77}{space 3}0.001{col 85}{space 4} .2178272{col 98}{space 3} .6732775
{txt}{hline 44}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. ** Model 2a
. svy: logit AI_improve i.aiexposure_gmyrek2023 if infullmodel_TableC1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,962
{txt}{col 1}Number of PSUs{col 20}= {res}    3,962{txt}{col 49}Population size{col 67}={res} 4,085.3712
{txt}{col 49}Design df{col 67}= {res}     3,961
{txt}{col 49}F({res}   3{txt},{res}   3959{txt}){col 67}= {res}      6.49
{txt}{col 49}Prob > F{col 67}= {res}    0.0002

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}  Linearized
{col 1}             AI_improve{col 25}{c |} Odds Ratio{col 37}   Std. Err.{col 49}      t{col 57}   P>|t|{col 65}     [95% Con{col 78}f. Interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}aiexposure_gmyrek2023 {c |}
{space 2}Automation Potential  {c |}{col 25}{res}{space 2} .4541972{col 37}{space 2} .1216989{col 48}{space 1}   -2.95{col 57}{space 3}0.003{col 65}{space 4} .2685971{col 78}{space 3} .7680466
{txt}Augmentation Potential  {c |}{col 25}{res}{space 2} 1.303874{col 37}{space 2} .2253171{col 48}{space 1}    1.54{col 57}{space 3}0.125{col 65}{space 4}  .929176{col 78}{space 3} 1.829672
{txt}{space 11}Big Unknown  {c |}{col 25}{res}{space 2}  1.34709{col 37}{space 2} .1873183{col 48}{space 1}    2.14{col 57}{space 3}0.032{col 65}{space 4} 1.025646{col 78}{space 3} 1.769277
{txt}{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .0955351{col 37}{space 2} .0097349{col 48}{space 1}  -23.04{col 57}{space 3}0.000{col 65}{space 4} .0782347{col 78}{space 3} .1166611
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 2b
. svy: logit AI_improve i.aiexposure_gmyrek2023 i.isco08_10group if infullmodel_TableC1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}note: 6.isco08_10group != 0 predicts failure perfectly
      6.isco08_10group dropped and 25 obs not used

Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,937
{txt}{col 1}Number of PSUs{col 20}= {res}    3,937{txt}{col 49}Population size{col 67}={res} 4,053.6686
{txt}{col 49}Design df{col 67}= {res}     3,936
{txt}{col 49}F({res}  10{txt},{res}   3927{txt}){col 67}= {res}      5.99
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 44}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 45}{c |}{col 57}  Linearized
{col 1}                                 AI_improve{col 45}{c |} Odds Ratio{col 57}   Std. Err.{col 69}      t{col 77}   P>|t|{col 85}     [95% Con{col 98}f. Interval]
{hline 44}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}aiexposure_gmyrek2023 {c |}
{space 22}Automation Potential  {c |}{col 45}{res}{space 2} .4970648{col 57}{space 2} .1715325{col 68}{space 1}   -2.03{col 77}{space 3}0.043{col 85}{space 4} .2526858{col 98}{space 3}  .977789
{txt}{space 20}Augmentation Potential  {c |}{col 45}{res}{space 2} 1.137015{col 57}{space 2} .2098294{col 68}{space 1}    0.70{col 77}{space 3}0.487{col 85}{space 4} .7918334{col 98}{space 3} 1.632671
{txt}{space 31}Big Unknown  {c |}{col 45}{res}{space 2} 1.124768{col 57}{space 2} .1751505{col 68}{space 1}    0.76{col 77}{space 3}0.450{col 85}{space 4} .8288418{col 98}{space 3} 1.526349
{txt}{space 43} {c |}
{space 29}isco08_10group {c |}
{space 29}Professionals  {c |}{col 45}{res}{space 2} .9542306{col 57}{space 2} .1830749{col 68}{space 1}   -0.24{col 77}{space 3}0.807{col 85}{space 4} .6550808{col 98}{space 3}  1.38999
{txt}{space 3}Technicians and Associate Professionals  {c |}{col 45}{res}{space 2} .6988454{col 57}{space 2} .1571137{col 68}{space 1}   -1.59{col 77}{space 3}0.111{col 85}{space 4} .4497343{col 98}{space 3} 1.085941
{txt}{space 18}Clerical Support Workers  {c |}{col 45}{res}{space 2} .5109544{col 57}{space 2} .1665436{col 68}{space 1}   -2.06{col 77}{space 3}0.039{col 85}{space 4} .2696814{col 98}{space 3} .9680845
{txt}{space 17}Service and Sales Workers  {c |}{col 45}{res}{space 2} .2698885{col 57}{space 2} .0840344{col 68}{space 1}   -4.21{col 77}{space 3}0.000{col 85}{space 4} .1465773{col 98}{space 3}  .496938
{txt}{space 14}Skilled Agricultural Workers  {c |}{col 45}{res}{space 2}        1{col 57}{txt}  (empty)
{space 10}Craft and Related Trades Workers  {c |}{col 45}{res}{space 2} .3553457{col 57}{space 2} .1575751{col 68}{space 1}   -2.33{col 77}{space 3}0.020{col 85}{space 4} .1489624{col 98}{space 3} .8476675
{txt}Plant and Machine Operators and Assemblers  {c |}{col 45}{res}{space 2} .3715529{col 57}{space 2} .1973096{col 68}{space 1}   -1.86{col 77}{space 3}0.062{col 85}{space 4} .1311775{col 98}{space 3} 1.052403
{txt}{space 20}Elementary Occupations  {c |}{col 45}{res}{space 2} .2777126{col 57}{space 2} .1141715{col 68}{space 1}   -3.12{col 77}{space 3}0.002{col 85}{space 4} .1240358{col 98}{space 3} .6217905
{txt}{space 43} {c |}
{space 38}_cons {c |}{col 45}{res}{space 2} .1619943{col 57}{space 2} .0291853{col 68}{space 1}  -10.10{col 77}{space 3}0.000{col 85}{space 4} .1137884{col 98}{space 3} .2306224
{txt}{hline 44}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 2c
. svy: logit AI_improve i.aiexposure_gmyrek2023 $controls_demographics $controls_occupindices if infullmodel_TableC1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}note: 6.isco08_10group != 0 predicts failure perfectly
      6.isco08_10group dropped and 25 obs not used

Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,937
{txt}{col 1}Number of PSUs{col 20}= {res}    3,937{txt}{col 49}Population size{col 67}={res} 4,053.6686
{txt}{col 49}Design df{col 67}= {res}     3,936
{txt}{col 49}F({res}  24{txt},{res}   3913{txt}){col 67}= {res}      7.52
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 44}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 45}{c |}{col 57}  Linearized
{col 1}                                 AI_improve{col 45}{c |} Odds Ratio{col 57}   Std. Err.{col 69}      t{col 77}   P>|t|{col 85}     [95% Con{col 98}f. Interval]
{hline 44}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}aiexposure_gmyrek2023 {c |}
{space 22}Automation Potential  {c |}{col 45}{res}{space 2} .4363968{col 57}{space 2} .1500973{col 68}{space 1}   -2.41{col 77}{space 3}0.016{col 85}{space 4}  .222343{col 98}{space 3} .8565241
{txt}{space 20}Augmentation Potential  {c |}{col 45}{res}{space 2} 1.102501{col 57}{space 2} .2072102{col 68}{space 1}    0.52{col 77}{space 3}0.604{col 85}{space 4} .7626941{col 98}{space 3} 1.593705
{txt}{space 31}Big Unknown  {c |}{col 45}{res}{space 2} 1.113335{col 57}{space 2} .1831705{col 68}{space 1}    0.65{col 77}{space 3}0.514{col 85}{space 4} .8063798{col 98}{space 3} 1.537136
{txt}{space 43} {c |}
{space 40}age {c |}{col 45}{res}{space 2} .9689655{col 57}{space 2} .0056303{col 68}{space 1}   -5.43{col 77}{space 3}0.000{col 85}{space 4} .9579896{col 98}{space 3} .9800671
{txt}{space 43} {c |}
{space 31}female_dummy {c |}
{space 38}Male  {c |}{col 45}{res}{space 2} 1.585122{col 57}{space 2} .2079093{col 68}{space 1}    3.51{col 77}{space 3}0.000{col 85}{space 4} 1.225695{col 98}{space 3}  2.04995
{txt}{space 43} {c |}
{space 31}degree_dummy {c |}
{space 29}Degree-Holder  {c |}{col 45}{res}{space 2}  1.72788{col 57}{space 2} .2386097{col 68}{space 1}    3.96{col 77}{space 3}0.000{col 85}{space 4} 1.318049{col 98}{space 3} 2.265142
{txt}{space 43} {c |}
{space 26}employmentstatus5 {c |}
{space 33}FT Worker  {c |}{col 45}{res}{space 2} 1.977495{col 57}{space 2} .4095913{col 68}{space 1}    3.29{col 77}{space 3}0.001{col 85}{space 4} 1.317513{col 98}{space 3} 2.968082
{txt}{space 43} {c |}
{space 30}employsector5 {c |}
{space 20}Public Sector Employee  {c |}{col 45}{res}{space 2} .7512721{col 57}{space 2} .1168736{col 68}{space 1}   -1.84{col 77}{space 3}0.066{col 85}{space 4} .5537799{col 98}{space 3} 1.019195
{txt}{space 7}Charity / Voluntary Sector Employee  {c |}{col 45}{res}{space 2} .7141189{col 57}{space 2} .2040552{col 68}{space 1}   -1.18{col 77}{space 3}0.239{col 85}{space 4} .4078213{col 98}{space 3} 1.250464
{txt}{space 13}Self-Employed / Company Owner  {c |}{col 45}{res}{space 2} 1.166811{col 57}{space 2} .2640677{col 68}{space 1}    0.68{col 77}{space 3}0.495{col 85}{space 4}  .748688{col 98}{space 3} 1.818444
{txt}{space 32}Other / DK  {c |}{col 45}{res}{space 2} 1.269626{col 57}{space 2} .6738974{col 68}{space 1}    0.45{col 77}{space 3}0.653{col 85}{space 4} .4484692{col 98}{space 3}  3.59434
{txt}{space 43} {c |}
{space 13}equivincomeHHquintile_HBAI_imp {c |}
{space 41}2  {c |}{col 45}{res}{space 2} .5171758{col 57}{space 2}  .153792{col 68}{space 1}   -2.22{col 77}{space 3}0.027{col 85}{space 4} .2886951{col 98}{space 3} .9264818
{txt}{space 41}3  {c |}{col 45}{res}{space 2} .4316609{col 57}{space 2} .1197524{col 68}{space 1}   -3.03{col 77}{space 3}0.002{col 85}{space 4} .2505692{col 98}{space 3} .7436314
{txt}{space 41}4  {c |}{col 45}{res}{space 2}  .491058{col 57}{space 2} .1220926{col 68}{space 1}   -2.86{col 77}{space 3}0.004{col 85}{space 4} .3016005{col 98}{space 3} .7995278
{txt}{space 30}Top Quintile  {c |}{col 45}{res}{space 2} .6503374{col 57}{space 2} .1553124{col 68}{space 1}   -1.80{col 77}{space 3}0.072{col 85}{space 4}  .407187{col 98}{space 3} 1.038685
{txt}{space 43} {c |}
{space 29}isco08_10group {c |}
{space 29}Professionals  {c |}{col 45}{res}{space 2} .8486199{col 57}{space 2} .1716602{col 68}{space 1}   -0.81{col 77}{space 3}0.417{col 85}{space 4} .5707919{col 98}{space 3} 1.261678
{txt}{space 3}Technicians and Associate Professionals  {c |}{col 45}{res}{space 2} .7823863{col 57}{space 2} .2198849{col 68}{space 1}   -0.87{col 77}{space 3}0.383{col 85}{space 4} .4509441{col 98}{space 3} 1.357438
{txt}{space 18}Clerical Support Workers  {c |}{col 45}{res}{space 2}  .738365{col 57}{space 2} .2866117{col 68}{space 1}   -0.78{col 77}{space 3}0.435{col 85}{space 4} .3449514{col 98}{space 3} 1.580463
{txt}{space 17}Service and Sales Workers  {c |}{col 45}{res}{space 2}  .443484{col 57}{space 2} .1580882{col 68}{space 1}   -2.28{col 77}{space 3}0.023{col 85}{space 4} .2204742{col 98}{space 3} .8920683
{txt}{space 14}Skilled Agricultural Workers  {c |}{col 45}{res}{space 2}        1{col 57}{txt}  (empty)
{space 10}Craft and Related Trades Workers  {c |}{col 45}{res}{space 2}  .446073{col 57}{space 2} .2104127{col 68}{space 1}   -1.71{col 77}{space 3}0.087{col 85}{space 4} .1769174{col 98}{space 3} 1.124712
{txt}Plant and Machine Operators and Assemblers  {c |}{col 45}{res}{space 2} .5245719{col 57}{space 2} .2969989{col 68}{space 1}   -1.14{col 77}{space 3}0.255{col 85}{space 4} .1728736{col 98}{space 3} 1.591774
{txt}{space 20}Elementary Occupations  {c |}{col 45}{res}{space 2} .4399616{col 57}{space 2} .2170649{col 68}{space 1}   -1.66{col 77}{space 3}0.096{col 85}{space 4} .1672346{col 98}{space 3} 1.157453
{txt}{space 43} {c |}
{space 30}std_RTI_Autor {c |}{col 45}{res}{space 2} .9159183{col 57}{space 2} .1893988{col 68}{space 1}   -0.42{col 77}{space 3}0.671{col 85}{space 4} .6106415{col 98}{space 3} 1.373812
{txt}{space 16}std_offshorable_Blinder2007 {c |}{col 45}{res}{space 2} 1.148308{col 57}{space 2} .0853038{col 68}{space 1}    1.86{col 77}{space 3}0.063{col 85}{space 4} .9926731{col 98}{space 3} 1.328344
{txt}{space 38}_cons {c |}{col 45}{res}{space 2}  .321383{col 57}{space 2} .1349424{col 68}{space 1}   -2.70{col 77}{space 3}0.007{col 85}{space 4} .1410946{col 98}{space 3} .7320413
{txt}{hline 44}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. ** Model 3a
. svy: logit AI_directionworse ib3.aiexposure_gmyrek2023 if infullmodel_TableC1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}   3{txt},{res}   1319{txt}){col 67}= {res}      8.90
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}  Linearized
{col 1}    AI_directionworse{col 23}{c |} Odds Ratio{col 35}   Std. Err.{col 47}      t{col 55}   P>|t|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
aiexposure_gmyrek2023 {c |}
{space 8}Not Affected  {c |}{col 23}{res}{space 2} 1.255641{col 35}{space 2} .2589383{col 46}{space 1}    1.10{col 55}{space 3}0.270{col 63}{space 4} .8378563{col 76}{space 3} 1.881747
{txt}Automation Potential  {c |}{col 23}{res}{space 2} 4.637831{col 35}{space 2} 1.423927{col 46}{space 1}    5.00{col 55}{space 3}0.000{col 63}{space 4} 2.539427{col 76}{space 3} 8.470209
{txt}{space 9}Big Unknown  {c |}{col 23}{res}{space 2} 1.355281{col 35}{space 2} .2705414{col 46}{space 1}    1.52{col 55}{space 3}0.128{col 63}{space 4} .9161292{col 76}{space 3} 2.004944
{txt}{space 21} {c |}
{space 16}_cons {c |}{col 23}{res}{space 2} 1.749315{col 35}{space 2}  .297136{col 46}{space 1}    3.29{col 55}{space 3}0.001{col 63}{space 4} 1.253579{col 76}{space 3} 2.441094
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 3b
. svy: logit AI_directionworse ib3.aiexposure_gmyrek2023 i.isco08_10group if infullmodel_TableC1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}note: 6.isco08_10group != 0 predicts success perfectly
      6.isco08_10group dropped and 2 obs not used

Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,320
{txt}{col 1}Number of PSUs{col 20}= {res}    1,320{txt}{col 49}Population size{col 67}={res} 1,347.7684
{txt}{col 49}Design df{col 67}= {res}     1,319
{txt}{col 49}F({res}  10{txt},{res}   1310{txt}){col 67}= {res}      4.10
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 44}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 45}{c |}{col 57}  Linearized
{col 1}                          AI_directionworse{col 45}{c |} Odds Ratio{col 57}   Std. Err.{col 69}      t{col 77}   P>|t|{col 85}     [95% Con{col 98}f. Interval]
{hline 44}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}aiexposure_gmyrek2023 {c |}
{space 30}Not Affected  {c |}{col 45}{res}{space 2} 1.184177{col 57}{space 2} .2591682{col 68}{space 1}    0.77{col 77}{space 3}0.440{col 85}{space 4} .7708181{col 98}{space 3} 1.819203
{txt}{space 22}Automation Potential  {c |}{col 45}{res}{space 2} 3.370009{col 57}{space 2} 1.281435{col 68}{space 1}    3.20{col 77}{space 3}0.001{col 85}{space 4} 1.598333{col 98}{space 3} 7.105505
{txt}{space 31}Big Unknown  {c |}{col 45}{res}{space 2} 1.267314{col 57}{space 2} .2583044{col 68}{space 1}    1.16{col 77}{space 3}0.245{col 85}{space 4}  .849636{col 98}{space 3} 1.890322
{txt}{space 43} {c |}
{space 29}isco08_10group {c |}
{space 29}Professionals  {c |}{col 45}{res}{space 2} 1.359617{col 57}{space 2} .3222527{col 68}{space 1}    1.30{col 77}{space 3}0.195{col 85}{space 4}  .854047{col 98}{space 3} 2.164469
{txt}{space 3}Technicians and Associate Professionals  {c |}{col 45}{res}{space 2} 1.607955{col 57}{space 2} .4417719{col 68}{space 1}    1.73{col 77}{space 3}0.084{col 85}{space 4} .9379907{col 98}{space 3} 2.756443
{txt}{space 18}Clerical Support Workers  {c |}{col 45}{res}{space 2} 2.172482{col 57}{space 2} .8067418{col 68}{space 1}    2.09{col 77}{space 3}0.037{col 85}{space 4} 1.048518{col 98}{space 3} 4.501285
{txt}{space 17}Service and Sales Workers  {c |}{col 45}{res}{space 2} 2.664701{col 57}{space 2}  .944672{col 68}{space 1}    2.76{col 77}{space 3}0.006{col 85}{space 4} 1.329257{col 98}{space 3} 5.341804
{txt}{space 14}Skilled Agricultural Workers  {c |}{col 45}{res}{space 2}        1{col 57}{txt}  (empty)
{space 10}Craft and Related Trades Workers  {c |}{col 45}{res}{space 2} 2.262772{col 57}{space 2} 1.159153{col 68}{space 1}    1.59{col 77}{space 3}0.111{col 85}{space 4} .8283129{col 98}{space 3} 6.181406
{txt}Plant and Machine Operators and Assemblers  {c |}{col 45}{res}{space 2}  2.06213{col 57}{space 2} 1.189306{col 68}{space 1}    1.25{col 77}{space 3}0.210{col 85}{space 4} .6651901{col 98}{space 3} 6.392726
{txt}{space 20}Elementary Occupations  {c |}{col 45}{res}{space 2} 2.810905{col 57}{space 2} 1.289553{col 68}{space 1}    2.25{col 77}{space 3}0.024{col 85}{space 4} 1.142835{col 98}{space 3} 6.913671
{txt}{space 43} {c |}
{space 38}_cons {c |}{col 45}{res}{space 2} 1.161549{col 57}{space 2}  .318299{col 68}{space 1}    0.55{col 77}{space 3}0.585{col 85}{space 4} .6785297{col 98}{space 3} 1.988412
{txt}{hline 44}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. ** Model 3c
. svy: logit AI_directionworse ib3.aiexposure_gmyrek2023 $controls_demographics $controls_occupindices if infullmodel_TableC1 == 1, or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}note: 6.isco08_10group != 0 predicts success perfectly
      6.isco08_10group dropped and 2 obs not used

Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,320
{txt}{col 1}Number of PSUs{col 20}= {res}    1,320{txt}{col 49}Population size{col 67}={res} 1,347.7684
{txt}{col 49}Design df{col 67}= {res}     1,319
{txt}{col 49}F({res}  24{txt},{res}   1296{txt}){col 67}= {res}      4.01
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 44}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 45}{c |}{col 57}  Linearized
{col 1}                          AI_directionworse{col 45}{c |} Odds Ratio{col 57}   Std. Err.{col 69}      t{col 77}   P>|t|{col 85}     [95% Con{col 98}f. Interval]
{hline 44}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}aiexposure_gmyrek2023 {c |}
{space 30}Not Affected  {c |}{col 45}{res}{space 2} 1.110727{col 57}{space 2} .2457395{col 68}{space 1}    0.47{col 77}{space 3}0.635{col 85}{space 4} .7196362{col 98}{space 3}  1.71436
{txt}{space 22}Automation Potential  {c |}{col 45}{res}{space 2} 3.441618{col 57}{space 2}  1.32233{col 68}{space 1}    3.22{col 77}{space 3}0.001{col 85}{space 4}  1.61963{col 98}{space 3} 7.313234
{txt}{space 31}Big Unknown  {c |}{col 45}{res}{space 2} 1.109062{col 57}{space 2} .2380516{col 68}{space 1}    0.48{col 77}{space 3}0.630{col 85}{space 4} .7279212{col 98}{space 3}  1.68977
{txt}{space 43} {c |}
{space 40}age {c |}{col 45}{res}{space 2} 1.022804{col 57}{space 2} .0065161{col 68}{space 1}    3.54{col 77}{space 3}0.000{col 85}{space 4} 1.010101{col 98}{space 3} 1.035668
{txt}{space 43} {c |}
{space 31}female_dummy {c |}
{space 38}Male  {c |}{col 45}{res}{space 2} .7491394{col 57}{space 2} .1104691{col 68}{space 1}   -1.96{col 77}{space 3}0.050{col 85}{space 4} .5609549{col 98}{space 3} 1.000454
{txt}{space 43} {c |}
{space 31}degree_dummy {c |}
{space 29}Degree-Holder  {c |}{col 45}{res}{space 2} .7445195{col 57}{space 2} .1149652{col 68}{space 1}   -1.91{col 77}{space 3}0.056{col 85}{space 4} .5499419{col 98}{space 3} 1.007942
{txt}{space 43} {c |}
{space 26}employmentstatus5 {c |}
{space 33}FT Worker  {c |}{col 45}{res}{space 2} .4593983{col 57}{space 2} .1071226{col 68}{space 1}   -3.34{col 77}{space 3}0.001{col 85}{space 4} .2907527{col 98}{space 3} .7258637
{txt}{space 43} {c |}
{space 30}employsector5 {c |}
{space 20}Public Sector Employee  {c |}{col 45}{res}{space 2} 1.315331{col 57}{space 2} .2376596{col 68}{space 1}    1.52{col 77}{space 3}0.130{col 85}{space 4} .9227732{col 98}{space 3} 1.874887
{txt}{space 7}Charity / Voluntary Sector Employee  {c |}{col 45}{res}{space 2} 1.299816{col 57}{space 2} .4271703{col 68}{space 1}    0.80{col 77}{space 3}0.425{col 85}{space 4} .6821609{col 98}{space 3}  2.47672
{txt}{space 13}Self-Employed / Company Owner  {c |}{col 45}{res}{space 2} .9951807{col 57}{space 2} .2728161{col 68}{space 1}   -0.02{col 77}{space 3}0.986{col 85}{space 4} .5812213{col 98}{space 3} 1.703971
{txt}{space 32}Other / DK  {c |}{col 45}{res}{space 2} .6482464{col 57}{space 2} .4000686{col 68}{space 1}   -0.70{col 77}{space 3}0.483{col 85}{space 4} .1931676{col 98}{space 3} 2.175435
{txt}{space 43} {c |}
{space 13}equivincomeHHquintile_HBAI_imp {c |}
{space 41}2  {c |}{col 45}{res}{space 2} 1.717031{col 57}{space 2}  .588583{col 68}{space 1}    1.58{col 77}{space 3}0.115{col 85}{space 4} .8764472{col 98}{space 3} 3.363802
{txt}{space 41}3  {c |}{col 45}{res}{space 2} 2.071509{col 57}{space 2} .6532091{col 68}{space 1}    2.31{col 77}{space 3}0.021{col 85}{space 4} 1.115914{col 98}{space 3} 3.845411
{txt}{space 41}4  {c |}{col 45}{res}{space 2} 1.827686{col 57}{space 2} .5258913{col 68}{space 1}    2.10{col 77}{space 3}0.036{col 85}{space 4} 1.039335{col 98}{space 3} 3.214016
{txt}{space 30}Top Quintile  {c |}{col 45}{res}{space 2} 1.421473{col 57}{space 2} .3960429{col 68}{space 1}    1.26{col 77}{space 3}0.207{col 85}{space 4} .8229313{col 98}{space 3}  2.45535
{txt}{space 43} {c |}
{space 29}isco08_10group {c |}
{space 29}Professionals  {c |}{col 45}{res}{space 2} 1.314303{col 57}{space 2} .3308287{col 68}{space 1}    1.09{col 77}{space 3}0.278{col 85}{space 4} .8021201{col 98}{space 3} 2.153535
{txt}{space 3}Technicians and Associate Professionals  {c |}{col 45}{res}{space 2} 1.056008{col 57}{space 2}  .371638{col 68}{space 1}    0.15{col 77}{space 3}0.877{col 85}{space 4} .5294572{col 98}{space 3} 2.106218
{txt}{space 18}Clerical Support Workers  {c |}{col 45}{res}{space 2} 1.041028{col 57}{space 2} .4857369{col 68}{space 1}    0.09{col 77}{space 3}0.931{col 85}{space 4} .4168045{col 98}{space 3} 2.600114
{txt}{space 17}Service and Sales Workers  {c |}{col 45}{res}{space 2} 1.659229{col 57}{space 2} .6816731{col 68}{space 1}    1.23{col 77}{space 3}0.218{col 85}{space 4} .7411052{col 98}{space 3} 3.714778
{txt}{space 14}Skilled Agricultural Workers  {c |}{col 45}{res}{space 2}        1{col 57}{txt}  (empty)
{space 10}Craft and Related Trades Workers  {c |}{col 45}{res}{space 2} 1.694142{col 57}{space 2} .9823366{col 68}{space 1}    0.91{col 77}{space 3}0.363{col 85}{space 4} .5431668{col 98}{space 3} 5.284045
{txt}Plant and Machine Operators and Assemblers  {c |}{col 45}{res}{space 2} 1.250009{col 57}{space 2} .7762619{col 68}{space 1}    0.36{col 77}{space 3}0.719{col 85}{space 4} .3696811{col 98}{space 3} 4.226677
{txt}{space 20}Elementary Occupations  {c |}{col 45}{res}{space 2} 1.179796{col 57}{space 2} .6990304{col 68}{space 1}    0.28{col 77}{space 3}0.780{col 85}{space 4} .3689822{col 98}{space 3} 3.772316
{txt}{space 43} {c |}
{space 30}std_RTI_Autor {c |}{col 45}{res}{space 2} 1.504887{col 57}{space 2} .3767832{col 68}{space 1}    1.63{col 77}{space 3}0.103{col 85}{space 4} .9208526{col 98}{space 3} 2.459334
{txt}{space 16}std_offshorable_Blinder2007 {c |}{col 45}{res}{space 2} .9240445{col 57}{space 2} .0799459{col 68}{space 1}   -0.91{col 77}{space 3}0.361{col 85}{space 4} .7797969{col 98}{space 3} 1.094975
{txt}{space 38}_cons {c |}{col 45}{res}{space 2} 1.101726{col 57}{space 2} .5889601{col 68}{space 1}    0.18{col 77}{space 3}0.856{col 85}{space 4} .3860297{col 98}{space 3} 3.144317
{txt}{hline 44}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. 
. 
. 
. ************************************************************************************************************
. 
. * APPENDIX D: Replication of Table 2 with Alternative Economic-Political Trends
. 
. *Set Conrols and Sample Size
. global controls_demographics i.age_3 i.female_dummy i.degree_dummy i.employmentstatus5 i.employsector5 i.equivincomeHHquintile_HBAI_imp i.isco08_5group
{txt}
{com}. global controls_objective aiexposure_felten2021 i.aiexposure_pizzinelli2023 i.aiexposure_gmyrek2023
{txt}
{com}. 
. 
. ************* TABLE D1: Demographic Predictors of Subjective Exposure to AI ***************
. 
. svy: logit AI_worsen aiexposure_felten2021  $controls_demographics $controls_objective, or
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}note: aiexposure_felten2021 omitted because of collinearity

Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      4.36
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.132371{col 50}{space 2} .1272744{col 61}{space 1}    1.11{col 70}{space 3}0.269{col 78}{space 4} .9084218{col 91}{space 3} 1.411529
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .9121425{col 50}{space 2}  .111979{col 61}{space 1}   -0.75{col 70}{space 3}0.454{col 78}{space 4} .7170231{col 91}{space 3} 1.160359
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .7963834{col 50}{space 2}  .107791{col 61}{space 1}   -1.68{col 70}{space 3}0.093{col 78}{space 4} .6107681{col 91}{space 3} 1.038408
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .8775447{col 50}{space 2} .0779128{col 61}{space 1}   -1.47{col 70}{space 3}0.141{col 78}{space 4} .7373476{col 91}{space 3} 1.044399
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  1.26827{col 50}{space 2}  .116173{col 61}{space 1}    2.59{col 70}{space 3}0.010{col 78}{space 4} 1.059786{col 91}{space 3} 1.517768
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.270618{col 50}{space 2} .1350353{col 61}{space 1}    2.25{col 70}{space 3}0.024{col 78}{space 4} 1.031634{col 91}{space 3} 1.564964
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9688247{col 50}{space 2} .0945639{col 61}{space 1}   -0.32{col 70}{space 3}0.746{col 78}{space 4} .8000861{col 91}{space 3}  1.17315
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9331544{col 50}{space 2} .1706257{col 61}{space 1}   -0.38{col 70}{space 3}0.705{col 78}{space 4} .6520264{col 91}{space 3} 1.335494
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9994992{col 50}{space 2} .1454519{col 61}{space 1}   -0.00{col 70}{space 3}0.997{col 78}{space 4} .7514043{col 91}{space 3} 1.329509
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8543309{col 50}{space 2} .2446505{col 61}{space 1}   -0.55{col 70}{space 3}0.583{col 78}{space 4} .4873009{col 91}{space 3} 1.497804
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}   1.0992{col 50}{space 2} .1850128{col 61}{space 1}    0.56{col 70}{space 3}0.574{col 78}{space 4} .7902459{col 91}{space 3} 1.528943
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.003071{col 50}{space 2} .1648735{col 61}{space 1}    0.02{col 70}{space 3}0.985{col 78}{space 4}  .726738{col 91}{space 3} 1.384476
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.023778{col 50}{space 2} .1646043{col 61}{space 1}    0.15{col 70}{space 3}0.884{col 78}{space 4} .7469762{col 91}{space 3} 1.403153
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.060449{col 50}{space 2} .1731385{col 61}{space 1}    0.36{col 70}{space 3}0.719{col 78}{space 4} .7699674{col 91}{space 3} 1.460519
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}   .94564{col 50}{space 2} .1172553{col 61}{space 1}   -0.45{col 70}{space 3}0.652{col 78}{space 4} .7415634{col 91}{space 3} 1.205878
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.000113{col 50}{space 2} .1883523{col 61}{space 1}    0.00{col 70}{space 3}1.000{col 78}{space 4} .6913415{col 91}{space 3} 1.446791
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .7392922{col 50}{space 2} .1278909{col 61}{space 1}   -1.75{col 70}{space 3}0.081{col 78}{space 4} .5266486{col 91}{space 3} 1.037794
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .8875048{col 50}{space 2} .2078441{col 61}{space 1}   -0.51{col 70}{space 3}0.610{col 78}{space 4} .5607477{col 91}{space 3} 1.404669
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2}        1{col 50}{txt}  (omitted)
{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.293345{col 50}{space 2} .3083182{col 61}{space 1}    1.08{col 70}{space 3}0.281{col 78}{space 4} .8104681{col 91}{space 3}  2.06392
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.012834{col 50}{space 2} .2390897{col 61}{space 1}    0.05{col 70}{space 3}0.957{col 78}{space 4} .6375913{col 91}{space 3}  1.60892
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2}  1.47375{col 50}{space 2} .3200393{col 61}{space 1}    1.79{col 70}{space 3}0.074{col 78}{space 4} .9627655{col 91}{space 3} 2.255938
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .8816561{col 50}{space 2}   .12698{col 61}{space 1}   -0.87{col 70}{space 3}0.382{col 78}{space 4} .6647648{col 91}{space 3} 1.169312
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.180469{col 50}{space 2} .1459234{col 61}{space 1}    1.34{col 70}{space 3}0.180{col 78}{space 4} .9264062{col 91}{space 3} 1.504208
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2431594{col 50}{space 2} .0630194{col 61}{space 1}   -5.46{col 70}{space 3}0.000{col 78}{space 4} .1462913{col 91}{space 3} .4041697
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. generate infullmodel_TableD1 = e(sample) 
{txt}
{com}.          
.          
. ******* Age Group ********
. 
. * Model 1: Worsen 
. svy: logit AI_worsen ib3.age_3 if infullmodel_TableD1 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   2{txt},{res}   3961{txt}){col 67}= {res}      2.96
{txt}{col 49}Prob > F{col 67}= {res}    0.0518

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}   AI_worsen{col 14}{c |} Odds Ratio{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}age_3group {c |}
{space 6}18-29  {c |}{col 14}{res}{space 2} 1.305605{col 26}{space 2} .1669743{col 37}{space 1}    2.09{col 46}{space 3}0.037{col 54}{space 4} 1.016057{col 67}{space 3} 1.677666
{txt}{space 6}30-49  {c |}{col 14}{res}{space 2}  1.19893{col 26}{space 2} .1071751{col 37}{space 1}    2.03{col 46}{space 3}0.042{col 54}{space 4}  1.00619{col 67}{space 3} 1.428591
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} .2698467{col 26}{space 2} .0189056{col 37}{space 1}  -18.70{col 46}{space 3}0.000{col 54}{space 4} .2352139{col 67}{space 3} .3095787
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_worsen ib3.age_3 $controls_demographics if infullmodel_TableD1 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      4.27
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}age_3group {c |}
{space 30}18-29  {c |}{col 38}{res}{space 2} 1.288983{col 50}{space 2} .1749154{col 61}{space 1}    1.87{col 70}{space 3}0.061{col 78}{space 4} .9878788{col 91}{space 3} 1.681864
{txt}{space 30}30-49  {c |}{col 38}{res}{space 2} 1.150756{col 50}{space 2} .1080209{col 61}{space 1}    1.50{col 70}{space 3}0.135{col 78}{space 4} .9573198{col 91}{space 3} 1.383278
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}  .870817{col 50}{space 2} .0765998{col 61}{space 1}   -1.57{col 70}{space 3}0.116{col 78}{space 4} .7328747{col 91}{space 3} 1.034723
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.282467{col 50}{space 2} .1163864{col 61}{space 1}    2.74{col 70}{space 3}0.006{col 78}{space 4} 1.073431{col 91}{space 3} 1.532209
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.279242{col 50}{space 2}  .134588{col 61}{space 1}    2.34{col 70}{space 3}0.019{col 78}{space 4}  1.04081{col 91}{space 3} 1.572296
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9047129{col 50}{space 2} .0865865{col 61}{space 1}   -1.05{col 70}{space 3}0.295{col 78}{space 4}   .74993{col 91}{space 3} 1.091442
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .8892559{col 50}{space 2} .1604124{col 61}{space 1}   -0.65{col 70}{space 3}0.515{col 78}{space 4} .6243557{col 91}{space 3} 1.266548
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9881668{col 50}{space 2} .1427731{col 61}{space 1}   -0.08{col 70}{space 3}0.934{col 78}{space 4} .7444041{col 91}{space 3} 1.311752
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8492572{col 50}{space 2} .2455706{col 61}{space 1}   -0.57{col 70}{space 3}0.572{col 78}{space 4} .4817605{col 91}{space 3} 1.497088
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.111529{col 50}{space 2} .1852479{col 61}{space 1}    0.63{col 70}{space 3}0.526{col 78}{space 4}  .801707{col 91}{space 3} 1.541082
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .9992331{col 50}{space 2} .1627154{col 61}{space 1}   -0.00{col 70}{space 3}0.996{col 78}{space 4} .7261303{col 91}{space 3} 1.375052
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.011691{col 50}{space 2} .1605068{col 61}{space 1}    0.07{col 70}{space 3}0.942{col 78}{space 4} .7412453{col 91}{space 3}  1.38081
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.081836{col 50}{space 2} .1754481{col 61}{space 1}    0.49{col 70}{space 3}0.628{col 78}{space 4} .7871807{col 91}{space 3} 1.486785
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.059863{col 50}{space 2} .1222794{col 61}{space 1}    0.50{col 70}{space 3}0.614{col 78}{space 4} .8453065{col 91}{space 3} 1.328878
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.498164{col 50}{space 2} .1787707{col 61}{space 1}    3.39{col 70}{space 3}0.001{col 78}{space 4} 1.185652{col 91}{space 3} 1.893046
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .6336916{col 50}{space 2} .0949955{col 61}{space 1}   -3.04{col 70}{space 3}0.002{col 78}{space 4} .4723206{col 91}{space 3} .8501958
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}  .599223{col 50}{space 2} .1024432{col 61}{space 1}   -3.00{col 70}{space 3}0.003{col 78}{space 4} .4285707{col 91}{space 3} .8378272
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2597675{col 50}{space 2} .0460782{col 61}{space 1}   -7.60{col 70}{space 3}0.000{col 78}{space 4} .1834638{col 91}{space 3} .3678062
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_worsen ib3.age_3 $controls_demographics  $controls_objective if infullmodel_TableD1 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      4.36
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}age_3group {c |}
{space 30}18-29  {c |}{col 38}{res}{space 2} 1.255677{col 50}{space 2} .1699566{col 61}{space 1}    1.68{col 70}{space 3}0.093{col 78}{space 4} .9630124{col 91}{space 3} 1.637283
{txt}{space 30}30-49  {c |}{col 38}{res}{space 2} 1.145356{col 50}{space 2} .1084957{col 61}{space 1}    1.43{col 70}{space 3}0.152{col 78}{space 4} .9512274{col 91}{space 3} 1.379103
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .8775447{col 50}{space 2} .0779128{col 61}{space 1}   -1.47{col 70}{space 3}0.141{col 78}{space 4} .7373476{col 91}{space 3} 1.044399
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  1.26827{col 50}{space 2}  .116173{col 61}{space 1}    2.59{col 70}{space 3}0.010{col 78}{space 4} 1.059786{col 91}{space 3} 1.517768
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.270618{col 50}{space 2} .1350353{col 61}{space 1}    2.25{col 70}{space 3}0.024{col 78}{space 4} 1.031634{col 91}{space 3} 1.564964
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9688247{col 50}{space 2} .0945639{col 61}{space 1}   -0.32{col 70}{space 3}0.746{col 78}{space 4} .8000861{col 91}{space 3}  1.17315
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9331544{col 50}{space 2} .1706257{col 61}{space 1}   -0.38{col 70}{space 3}0.705{col 78}{space 4} .6520264{col 91}{space 3} 1.335494
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9994992{col 50}{space 2} .1454519{col 61}{space 1}   -0.00{col 70}{space 3}0.997{col 78}{space 4} .7514043{col 91}{space 3} 1.329509
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8543309{col 50}{space 2} .2446505{col 61}{space 1}   -0.55{col 70}{space 3}0.583{col 78}{space 4} .4873009{col 91}{space 3} 1.497804
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}   1.0992{col 50}{space 2} .1850128{col 61}{space 1}    0.56{col 70}{space 3}0.574{col 78}{space 4} .7902459{col 91}{space 3} 1.528943
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.003071{col 50}{space 2} .1648735{col 61}{space 1}    0.02{col 70}{space 3}0.985{col 78}{space 4}  .726738{col 91}{space 3} 1.384476
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.023778{col 50}{space 2} .1646043{col 61}{space 1}    0.15{col 70}{space 3}0.884{col 78}{space 4} .7469762{col 91}{space 3} 1.403153
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.060449{col 50}{space 2} .1731385{col 61}{space 1}    0.36{col 70}{space 3}0.719{col 78}{space 4} .7699674{col 91}{space 3} 1.460519
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}   .94564{col 50}{space 2} .1172553{col 61}{space 1}   -0.45{col 70}{space 3}0.652{col 78}{space 4} .7415634{col 91}{space 3} 1.205878
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.000113{col 50}{space 2} .1883523{col 61}{space 1}    0.00{col 70}{space 3}1.000{col 78}{space 4} .6913415{col 91}{space 3} 1.446791
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .7392922{col 50}{space 2} .1278909{col 61}{space 1}   -1.75{col 70}{space 3}0.081{col 78}{space 4} .5266486{col 91}{space 3} 1.037794
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .8875048{col 50}{space 2} .2078441{col 61}{space 1}   -0.51{col 70}{space 3}0.610{col 78}{space 4} .5607477{col 91}{space 3} 1.404669
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.132371{col 50}{space 2} .1272744{col 61}{space 1}    1.11{col 70}{space 3}0.269{col 78}{space 4} .9084218{col 91}{space 3} 1.411529
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.293345{col 50}{space 2} .3083182{col 61}{space 1}    1.08{col 70}{space 3}0.281{col 78}{space 4} .8104681{col 91}{space 3}  2.06392
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.012834{col 50}{space 2} .2390897{col 61}{space 1}    0.05{col 70}{space 3}0.957{col 78}{space 4} .6375913{col 91}{space 3}  1.60892
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2}  1.47375{col 50}{space 2} .3200393{col 61}{space 1}    1.79{col 70}{space 3}0.074{col 78}{space 4} .9627655{col 91}{space 3} 2.255938
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .8816561{col 50}{space 2}   .12698{col 61}{space 1}   -0.87{col 70}{space 3}0.382{col 78}{space 4} .6647648{col 91}{space 3} 1.169312
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.180469{col 50}{space 2} .1459234{col 61}{space 1}    1.34{col 70}{space 3}0.180{col 78}{space 4} .9264062{col 91}{space 3} 1.504208
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .1936482{col 50}{space 2}  .049402{col 61}{space 1}   -6.44{col 70}{space 3}0.000{col 78}{space 4} .1174343{col 91}{space 3} .3193242
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 2: Improve 
. svy: logit AI_improve ib3.age_3 if infullmodel_TableD1 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   2{txt},{res}   3961{txt}){col 67}= {res}     15.89
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}  AI_improve{col 14}{c |} Odds Ratio{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}age_3group {c |}
{space 6}18-29  {c |}{col 14}{res}{space 2} 2.742206{col 26}{space 2} .4994177{col 37}{space 1}    5.54{col 46}{space 3}0.000{col 54}{space 4}   1.9188{col 67}{space 3} 3.918957
{txt}{space 6}30-49  {c |}{col 14}{res}{space 2} 1.789594{col 26}{space 2} .2620354{col 37}{space 1}    3.97{col 46}{space 3}0.000{col 54}{space 4} 1.343018{col 67}{space 3} 2.384663
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} .0626806{col 26}{space 2} .0076591{col 37}{space 1}  -22.67{col 46}{space 3}0.000{col 54}{space 4} .0493278{col 67}{space 3}  .079648
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_improve ib3.age_3 $controls_demographics if infullmodel_TableD1 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      9.00
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                          AI_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}age_3group {c |}
{space 30}18-29  {c |}{col 38}{res}{space 2} 2.318133{col 50}{space 2} .4561616{col 61}{space 1}    4.27{col 70}{space 3}0.000{col 78}{space 4} 1.576116{col 91}{space 3} 3.409485
{txt}{space 30}30-49  {c |}{col 38}{res}{space 2} 1.358249{col 50}{space 2}  .214506{col 61}{space 1}    1.94{col 70}{space 3}0.053{col 78}{space 4} .9965741{col 91}{space 3} 1.851183
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6274358{col 50}{space 2} .0815649{col 61}{space 1}   -3.59{col 70}{space 3}0.000{col 78}{space 4} .4862747{col 91}{space 3} .8095748
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.753261{col 50}{space 2} .2386836{col 61}{space 1}    4.12{col 70}{space 3}0.000{col 78}{space 4} 1.342552{col 91}{space 3} 2.289613
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .4766213{col 50}{space 2} .0975186{col 61}{space 1}   -3.62{col 70}{space 3}0.000{col 78}{space 4} .3191252{col 91}{space 3} .7118456
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2}  .703992{col 50}{space 2} .1022251{col 61}{space 1}   -2.42{col 70}{space 3}0.016{col 78}{space 4} .5295767{col 91}{space 3} .9358507
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .6980172{col 50}{space 2} .1996649{col 61}{space 1}   -1.26{col 70}{space 3}0.209{col 78}{space 4} .3983906{col 91}{space 3} 1.222991
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.045743{col 50}{space 2} .2381017{col 61}{space 1}    0.20{col 70}{space 3}0.844{col 78}{space 4} .6692037{col 91}{space 3} 1.634148
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.253748{col 50}{space 2} .6701505{col 61}{space 1}    0.42{col 70}{space 3}0.672{col 78}{space 4} .4396317{col 91}{space 3} 3.575457
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5114603{col 50}{space 2} .1552909{col 61}{space 1}   -2.21{col 70}{space 3}0.027{col 78}{space 4} .2820259{col 91}{space 3} .9275449
{txt}{space 34}3  {c |}{col 38}{res}{space 2}  .431203{col 50}{space 2} .1200384{col 61}{space 1}   -3.02{col 70}{space 3}0.003{col 78}{space 4} .2498341{col 91}{space 3} .7442382
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .4891384{col 50}{space 2} .1216417{col 61}{space 1}   -2.88{col 70}{space 3}0.004{col 78}{space 4}   .30039{col 91}{space 3} .7964859
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .6643646{col 50}{space 2} .1598227{col 61}{space 1}   -1.70{col 70}{space 3}0.089{col 78}{space 4} .4145481{col 91}{space 3} 1.064727
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .8602597{col 50}{space 2} .1431378{col 61}{space 1}   -0.90{col 70}{space 3}0.366{col 78}{space 4} .6208056{col 91}{space 3} 1.192075
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}  .483637{col 50}{space 2} .1091257{col 61}{space 1}   -3.22{col 70}{space 3}0.001{col 78}{space 4} .3107417{col 91}{space 3} .7527305
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .4243676{col 50}{space 2} .1168096{col 61}{space 1}   -3.11{col 70}{space 3}0.002{col 78}{space 4} .2473845{col 91}{space 3} .7279674
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .3840638{col 50}{space 2} .1048289{col 61}{space 1}   -3.51{col 70}{space 3}0.000{col 78}{space 4} .2249053{col 91}{space 3} .6558537
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2036033{col 50}{space 2}  .054992{col 61}{space 1}   -5.89{col 70}{space 3}0.000{col 78}{space 4} .1198976{col 91}{space 3} .3457477
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_improve ib3.age_3 $controls_demographics $controls_objective if infullmodel_TableD1 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      7.85
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                          AI_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}age_3group {c |}
{space 30}18-29  {c |}{col 38}{res}{space 2} 2.277934{col 50}{space 2} .4508852{col 61}{space 1}    4.16{col 70}{space 3}0.000{col 78}{space 4} 1.545276{col 91}{space 3} 3.357964
{txt}{space 30}30-49  {c |}{col 38}{res}{space 2} 1.359409{col 50}{space 2} .2158074{col 61}{space 1}    1.93{col 70}{space 3}0.053{col 78}{space 4} .9958173{col 91}{space 3} 1.855754
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6317683{col 50}{space 2} .0833817{col 61}{space 1}   -3.48{col 70}{space 3}0.001{col 78}{space 4} .4877314{col 91}{space 3} .8183422
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.732196{col 50}{space 2} .2368375{col 61}{space 1}    4.02{col 70}{space 3}0.000{col 78}{space 4} 1.324889{col 91}{space 3} 2.264721
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .4842575{col 50}{space 2} .0992227{col 61}{space 1}   -3.54{col 70}{space 3}0.000{col 78}{space 4} .3240521{col 91}{space 3} .7236656
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .6974604{col 50}{space 2} .1028677{col 61}{space 1}   -2.44{col 70}{space 3}0.015{col 78}{space 4} .5223221{col 91}{space 3} .9313239
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .6836831{col 50}{space 2} .1929683{col 61}{space 1}   -1.35{col 70}{space 3}0.178{col 78}{space 4} .3931256{col 91}{space 3}  1.18899
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.129698{col 50}{space 2} .2598135{col 61}{space 1}    0.53{col 70}{space 3}0.596{col 78}{space 4} .7196789{col 91}{space 3} 1.773314
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.339877{col 50}{space 2} .7204718{col 61}{space 1}    0.54{col 70}{space 3}0.586{col 78}{space 4} .4668973{col 91}{space 3} 3.845108
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5083028{col 50}{space 2} .1523285{col 61}{space 1}   -2.26{col 70}{space 3}0.024{col 78}{space 4} .2824594{col 91}{space 3}  .914722
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .4113217{col 50}{space 2} .1135379{col 61}{space 1}   -3.22{col 70}{space 3}0.001{col 78}{space 4} .2394149{col 91}{space 3} .7066626
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .4648291{col 50}{space 2} .1149079{col 61}{space 1}   -3.10{col 70}{space 3}0.002{col 78}{space 4} .2862912{col 91}{space 3} .7547074
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .6062726{col 50}{space 2} .1449004{col 61}{space 1}   -2.09{col 70}{space 3}0.036{col 78}{space 4} .3794607{col 91}{space 3} .9686549
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.010877{col 50}{space 2} .1838329{col 61}{space 1}    0.06{col 70}{space 3}0.953{col 78}{space 4} .7077112{col 91}{space 3} 1.443913
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.090424{col 50}{space 2} .3315547{col 61}{space 1}    0.28{col 70}{space 3}0.776{col 78}{space 4} .6007582{col 91}{space 3} 1.979207
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .7050556{col 50}{space 2} .2294216{col 61}{space 1}   -1.07{col 70}{space 3}0.283{col 78}{space 4} .3725306{col 91}{space 3} 1.334396
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}  .859264{col 50}{space 2} .2912932{col 61}{space 1}   -0.45{col 70}{space 3}0.655{col 78}{space 4} .4420572{col 91}{space 3} 1.670224
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.600942{col 50}{space 2} .2810704{col 61}{space 1}    2.68{col 70}{space 3}0.007{col 78}{space 4} 1.134718{col 91}{space 3} 2.258723
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.005969{col 50}{space 2} .3909627{col 61}{space 1}    0.02{col 70}{space 3}0.988{col 78}{space 4} .4695373{col 91}{space 3} 2.155257
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2}  1.09556{col 50}{space 2} .4040863{col 61}{space 1}    0.25{col 70}{space 3}0.805{col 78}{space 4} .5315977{col 91}{space 3}  2.25782
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .3170697{col 50}{space 2}  .116649{col 61}{space 1}   -3.12{col 70}{space 3}0.002{col 78}{space 4} .1541361{col 91}{space 3} .6522364
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.003973{col 50}{space 2} .2001391{col 61}{space 1}    0.02{col 70}{space 3}0.984{col 78}{space 4} .6791814{col 91}{space 3} 1.484084
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2}   .88363{col 50}{space 2} .1492986{col 61}{space 1}   -0.73{col 70}{space 3}0.464{col 78}{space 4}  .634464{col 91}{space 3} 1.230648
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .1342537{col 50}{space 2} .0481985{col 61}{space 1}   -5.59{col 70}{space 3}0.000{col 78}{space 4} .0664114{col 91}{space 3} .2714002
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 3: Worsen v Improve
. svy: logit AI_directionworse ib3.age_3 if infullmodel_TableD1 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}   2{txt},{res}   1320{txt}){col 67}= {res}      6.21
{txt}{col 49}Prob > F{col 67}= {res}    0.0021

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}  Linearized
{col 1}AI_directionworse{col 19}{c |} Odds Ratio{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}age_3group {c |}
{space 11}18-29  {c |}{col 19}{res}{space 2}  .493023{col 31}{space 2} .1008862{col 42}{space 1}   -3.46{col 51}{space 3}0.001{col 59}{space 4} .3300106{col 72}{space 3} .7365571
{txt}{space 11}30-49  {c |}{col 19}{res}{space 2} .6727093{col 31}{space 2} .1085475{col 42}{space 1}   -2.46{col 51}{space 3}0.014{col 59}{space 4} .4901767{col 72}{space 3} .9232135
{txt}{space 17} {c |}
{space 12}_cons {c |}{col 19}{res}{space 2}  3.60276{col 31}{space 2}  .481712{col 42}{space 1}    9.59{col 51}{space 3}0.000{col 59}{space 4} 2.771532{col 72}{space 3} 4.683287
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_directionworse ib3.age_3 $controls_demographics if infullmodel_TableD1 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}  17{txt},{res}   1305{txt}){col 67}= {res}      4.64
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                   AI_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}age_3group {c |}
{space 30}18-29  {c |}{col 38}{res}{space 2} .5776636{col 50}{space 2} .1231551{col 61}{space 1}   -2.57{col 70}{space 3}0.010{col 78}{space 4} .3802213{col 91}{space 3} .8776341
{txt}{space 30}30-49  {c |}{col 38}{res}{space 2} .9420499{col 50}{space 2} .1677544{col 61}{space 1}   -0.34{col 70}{space 3}0.737{col 78}{space 4} .6642907{col 91}{space 3} 1.335948
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} 1.343212{col 50}{space 2} .1961699{col 61}{space 1}    2.02{col 70}{space 3}0.044{col 78}{space 4} 1.008595{col 91}{space 3} 1.788845
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7325523{col 50}{space 2} .1120903{col 61}{space 1}   -2.03{col 70}{space 3}0.042{col 78}{space 4} .5425927{col 91}{space 3}  .989016
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 2.387622{col 50}{space 2} .5475305{col 61}{space 1}    3.80{col 70}{space 3}0.000{col 78}{space 4}  1.52261{col 91}{space 3} 3.744057
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.240947{col 50}{space 2} .2031517{col 61}{space 1}    1.32{col 70}{space 3}0.188{col 78}{space 4} .9000731{col 91}{space 3} 1.710916
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}  1.22502{col 50}{space 2} .4068413{col 61}{space 1}    0.61{col 70}{space 3}0.541{col 78}{space 4} .6385446{col 91}{space 3} 2.350146
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.053222{col 50}{space 2} .2908356{col 61}{space 1}    0.19{col 70}{space 3}0.851{col 78}{space 4} .6127091{col 91}{space 3} 1.810445
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .6364657{col 50}{space 2}  .396653{col 61}{space 1}   -0.72{col 70}{space 3}0.469{col 78}{space 4} .1874172{col 91}{space 3} 2.161426
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.921637{col 50}{space 2} .6822025{col 61}{space 1}    1.84{col 70}{space 3}0.066{col 78}{space 4} .9576527{col 91}{space 3} 3.855977
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 2.194025{col 50}{space 2} .7060782{col 61}{space 1}    2.44{col 70}{space 3}0.015{col 78}{space 4} 1.166965{col 91}{space 3} 4.125013
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.954344{col 50}{space 2} .5733307{col 61}{space 1}    2.28{col 70}{space 3}0.023{col 78}{space 4} 1.099161{col 91}{space 3} 3.474884
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.511639{col 50}{space 2} .4308267{col 61}{space 1}    1.45{col 70}{space 3}0.147{col 78}{space 4} .8642265{col 91}{space 3} 2.644042
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.146499{col 50}{space 2} .2142211{col 61}{space 1}    0.73{col 70}{space 3}0.464{col 78}{space 4} .7946625{col 91}{space 3} 1.654112
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 2.509639{col 50}{space 2} .6072188{col 61}{space 1}    3.80{col 70}{space 3}0.000{col 78}{space 4} 1.561242{col 91}{space 3} 4.034152
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.649087{col 50}{space 2} .5168764{col 61}{space 1}    1.60{col 70}{space 3}0.111{col 78}{space 4}  .891672{col 91}{space 3} 3.049874
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.619572{col 50}{space 2} .4988888{col 61}{space 1}    1.57{col 70}{space 3}0.118{col 78}{space 4} .8850298{col 91}{space 3} 2.963757
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 1.158567{col 50}{space 2} .3855995{col 61}{space 1}    0.44{col 70}{space 3}0.658{col 78}{space 4}   .60306{col 91}{space 3} 2.225777
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_directionworse ib3.age_3 $controls_demographics $controls_objective if infullmodel_TableD1 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}  23{txt},{res}   1299{txt}){col 67}= {res}      4.25
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                   AI_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}age_3group {c |}
{space 30}18-29  {c |}{col 38}{res}{space 2}   .58192{col 50}{space 2} .1248418{col 61}{space 1}   -2.52{col 70}{space 3}0.012{col 78}{space 4} .3820181{col 91}{space 3} .8864264
{txt}{space 30}30-49  {c |}{col 38}{res}{space 2} .9565765{col 50}{space 2} .1712169{col 61}{space 1}   -0.25{col 70}{space 3}0.804{col 78}{space 4}  .673324{col 91}{space 3} 1.358987
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} 1.335884{col 50}{space 2} .1975336{col 61}{space 1}    1.96{col 70}{space 3}0.050{col 78}{space 4} .9995127{col 91}{space 3} 1.785456
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7356201{col 50}{space 2} .1132835{col 61}{space 1}   -1.99{col 70}{space 3}0.046{col 78}{space 4} .5438144{col 91}{space 3} .9950766
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 2.412895{col 50}{space 2} .5516282{col 61}{space 1}    3.85{col 70}{space 3}0.000{col 78}{space 4} 1.540852{col 91}{space 3} 3.778468
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.315665{col 50}{space 2} .2214155{col 61}{space 1}    1.63{col 70}{space 3}0.103{col 78}{space 4} .9457228{col 91}{space 3} 1.830319
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.318259{col 50}{space 2} .4386386{col 61}{space 1}    0.83{col 70}{space 3}0.406{col 78}{space 4}  .686296{col 91}{space 3} 2.532153
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2}  .926384{col 50}{space 2} .2584575{col 61}{space 1}   -0.27{col 70}{space 3}0.784{col 78}{space 4} .5359093{col 91}{space 3} 1.601367
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5966104{col 50}{space 2} .3877683{col 61}{space 1}   -0.79{col 70}{space 3}0.427{col 78}{space 4} .1667029{col 91}{space 3}   2.1352
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.823815{col 50}{space 2} .6373137{col 61}{space 1}    1.72{col 70}{space 3}0.086{col 78}{space 4} .9188914{col 91}{space 3} 3.619906
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 2.220856{col 50}{space 2} .7064391{col 61}{space 1}    2.51{col 70}{space 3}0.012{col 78}{space 4} 1.189901{col 91}{space 3} 4.145053
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.988575{col 50}{space 2} .5775212{col 61}{space 1}    2.37{col 70}{space 3}0.018{col 78}{space 4} 1.124889{col 91}{space 3} 3.515397
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.569265{col 50}{space 2} .4418033{col 61}{space 1}    1.60{col 70}{space 3}0.110{col 78}{space 4} .9033028{col 91}{space 3}  2.72621
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}  .926379{col 50}{space 2} .1941269{col 61}{space 1}   -0.36{col 70}{space 3}0.715{col 78}{space 4} .6141183{col 91}{space 3} 1.397415
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} .8738151{col 50}{space 2} .3061096{col 61}{space 1}   -0.39{col 70}{space 3}0.700{col 78}{space 4} .4394994{col 91}{space 3} 1.737324
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.107186{col 50}{space 2} .4118154{col 61}{space 1}    0.27{col 70}{space 3}0.784{col 78}{space 4} .5337372{col 91}{space 3} 2.296748
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .9733152{col 50}{space 2}   .39149{col 61}{space 1}   -0.07{col 70}{space 3}0.946{col 78}{space 4} .4421467{col 91}{space 3} 2.142597
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .7403207{col 50}{space 2} .1530683{col 61}{space 1}   -1.45{col 70}{space 3}0.146{col 78}{space 4} .4934744{col 91}{space 3} 1.110645
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.080125{col 50}{space 2} .4675268{col 61}{space 1}    0.18{col 70}{space 3}0.859{col 78}{space 4} .4620594{col 91}{space 3} 2.524936
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} .8527179{col 50}{space 2} .3655586{col 61}{space 1}   -0.37{col 70}{space 3}0.710{col 78}{space 4} .3677581{col 91}{space 3} 1.977191
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} 3.897365{col 50}{space 2}  1.55288{col 61}{space 1}    3.41{col 70}{space 3}0.001{col 78}{space 4} 1.783628{col 91}{space 3} 8.516043
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .9586039{col 50}{space 2} .2224665{col 61}{space 1}   -0.18{col 70}{space 3}0.855{col 78}{space 4} .6080186{col 91}{space 3} 1.511338
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.227994{col 50}{space 2} .2313644{col 61}{space 1}    1.09{col 70}{space 3}0.276{col 78}{space 4} .8485466{col 91}{space 3}  1.77712
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 1.488882{col 50}{space 2} .6466984{col 61}{space 1}    0.92{col 70}{space 3}0.360{col 78}{space 4} .6350391{col 91}{space 3} 3.490761
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. 
. ******* Gender ********
. 
. * Model 1: Worsen 
. svy: logit AI_worsen ib1.female_dummy if infullmodel_TableD1 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   1{txt},{res}   3962{txt}){col 67}= {res}      0.21
{txt}{col 49}Prob > F{col 67}= {res}    0.6446

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}   AI_worsen{col 14}{c |} Odds Ratio{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
female_dummy {c |}
{space 7}Male  {c |}{col 14}{res}{space 2} 1.037883{col 26}{space 2}  .083657{col 37}{space 1}    0.46{col 46}{space 3}0.645{col 54}{space 4} .8861707{col 67}{space 3} 1.215568
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .3038898{col 26}{space 2} .0161685{col 37}{space 1}  -22.39{col 46}{space 3}0.000{col 54}{space 4} .2737878{col 67}{space 3} .3373015
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_worsen ib1.female_dummy $controls_demographics if infullmodel_TableD1 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      4.27
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.148347{col 50}{space 2} .1010121{col 61}{space 1}    1.57{col 70}{space 3}0.116{col 78}{space 4} .9664422{col 91}{space 3}  1.36449
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .8927627{col 50}{space 2} .1100534{col 61}{space 1}   -0.92{col 70}{space 3}0.358{col 78}{space 4} .7010902{col 91}{space 3} 1.136837
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .7758053{col 50}{space 2}  .105277{col 61}{space 1}   -1.87{col 70}{space 3}0.061{col 78}{space 4} .5945784{col 91}{space 3}  1.01227
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.282467{col 50}{space 2} .1163864{col 61}{space 1}    2.74{col 70}{space 3}0.006{col 78}{space 4} 1.073431{col 91}{space 3} 1.532209
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.279242{col 50}{space 2}  .134588{col 61}{space 1}    2.34{col 70}{space 3}0.019{col 78}{space 4}  1.04081{col 91}{space 3} 1.572296
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9047129{col 50}{space 2} .0865865{col 61}{space 1}   -1.05{col 70}{space 3}0.295{col 78}{space 4}   .74993{col 91}{space 3} 1.091442
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .8892559{col 50}{space 2} .1604124{col 61}{space 1}   -0.65{col 70}{space 3}0.515{col 78}{space 4} .6243557{col 91}{space 3} 1.266548
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9881668{col 50}{space 2} .1427731{col 61}{space 1}   -0.08{col 70}{space 3}0.934{col 78}{space 4} .7444041{col 91}{space 3} 1.311752
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8492572{col 50}{space 2} .2455706{col 61}{space 1}   -0.57{col 70}{space 3}0.572{col 78}{space 4} .4817605{col 91}{space 3} 1.497088
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.111529{col 50}{space 2} .1852479{col 61}{space 1}    0.63{col 70}{space 3}0.526{col 78}{space 4}  .801707{col 91}{space 3} 1.541082
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .9992331{col 50}{space 2} .1627154{col 61}{space 1}   -0.00{col 70}{space 3}0.996{col 78}{space 4} .7261303{col 91}{space 3} 1.375052
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.011691{col 50}{space 2} .1605068{col 61}{space 1}    0.07{col 70}{space 3}0.942{col 78}{space 4} .7412453{col 91}{space 3}  1.38081
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.081836{col 50}{space 2} .1754481{col 61}{space 1}    0.49{col 70}{space 3}0.628{col 78}{space 4} .7871807{col 91}{space 3} 1.486785
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.059863{col 50}{space 2} .1222794{col 61}{space 1}    0.50{col 70}{space 3}0.614{col 78}{space 4} .8453065{col 91}{space 3} 1.328878
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.498164{col 50}{space 2} .1787707{col 61}{space 1}    3.39{col 70}{space 3}0.001{col 78}{space 4} 1.185652{col 91}{space 3} 1.893046
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .6336916{col 50}{space 2} .0949955{col 61}{space 1}   -3.04{col 70}{space 3}0.002{col 78}{space 4} .4723206{col 91}{space 3} .8501958
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}  .599223{col 50}{space 2} .1024432{col 61}{space 1}   -3.00{col 70}{space 3}0.003{col 78}{space 4} .4285707{col 91}{space 3} .8378272
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2915808{col 50}{space 2} .0557617{col 61}{space 1}   -6.44{col 70}{space 3}0.000{col 78}{space 4}  .200413{col 91}{space 3} .4242208
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_worsen ib1.female_dummy $controls_demographics  $controls_objective if infullmodel_TableD1 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      4.36
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.139543{col 50}{space 2} .1011743{col 61}{space 1}    1.47{col 70}{space 3}0.141{col 78}{space 4} .9574888{col 91}{space 3} 1.356213
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .9121425{col 50}{space 2}  .111979{col 61}{space 1}   -0.75{col 70}{space 3}0.454{col 78}{space 4} .7170231{col 91}{space 3} 1.160359
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .7963834{col 50}{space 2}  .107791{col 61}{space 1}   -1.68{col 70}{space 3}0.093{col 78}{space 4} .6107681{col 91}{space 3} 1.038408
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  1.26827{col 50}{space 2}  .116173{col 61}{space 1}    2.59{col 70}{space 3}0.010{col 78}{space 4} 1.059786{col 91}{space 3} 1.517768
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.270618{col 50}{space 2} .1350353{col 61}{space 1}    2.25{col 70}{space 3}0.024{col 78}{space 4} 1.031634{col 91}{space 3} 1.564964
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9688247{col 50}{space 2} .0945639{col 61}{space 1}   -0.32{col 70}{space 3}0.746{col 78}{space 4} .8000861{col 91}{space 3}  1.17315
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9331544{col 50}{space 2} .1706257{col 61}{space 1}   -0.38{col 70}{space 3}0.705{col 78}{space 4} .6520264{col 91}{space 3} 1.335494
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9994992{col 50}{space 2} .1454519{col 61}{space 1}   -0.00{col 70}{space 3}0.997{col 78}{space 4} .7514043{col 91}{space 3} 1.329509
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8543309{col 50}{space 2} .2446505{col 61}{space 1}   -0.55{col 70}{space 3}0.583{col 78}{space 4} .4873009{col 91}{space 3} 1.497804
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}   1.0992{col 50}{space 2} .1850128{col 61}{space 1}    0.56{col 70}{space 3}0.574{col 78}{space 4} .7902459{col 91}{space 3} 1.528943
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.003071{col 50}{space 2} .1648735{col 61}{space 1}    0.02{col 70}{space 3}0.985{col 78}{space 4}  .726738{col 91}{space 3} 1.384476
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.023778{col 50}{space 2} .1646043{col 61}{space 1}    0.15{col 70}{space 3}0.884{col 78}{space 4} .7469762{col 91}{space 3} 1.403153
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.060449{col 50}{space 2} .1731385{col 61}{space 1}    0.36{col 70}{space 3}0.719{col 78}{space 4} .7699674{col 91}{space 3} 1.460519
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}   .94564{col 50}{space 2} .1172553{col 61}{space 1}   -0.45{col 70}{space 3}0.652{col 78}{space 4} .7415634{col 91}{space 3} 1.205878
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.000113{col 50}{space 2} .1883523{col 61}{space 1}    0.00{col 70}{space 3}1.000{col 78}{space 4} .6913415{col 91}{space 3} 1.446791
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .7392922{col 50}{space 2} .1278909{col 61}{space 1}   -1.75{col 70}{space 3}0.081{col 78}{space 4} .5266486{col 91}{space 3} 1.037794
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .8875048{col 50}{space 2} .2078441{col 61}{space 1}   -0.51{col 70}{space 3}0.610{col 78}{space 4} .5607477{col 91}{space 3} 1.404669
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.132371{col 50}{space 2} .1272744{col 61}{space 1}    1.11{col 70}{space 3}0.269{col 78}{space 4} .9084218{col 91}{space 3} 1.411529
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.293345{col 50}{space 2} .3083182{col 61}{space 1}    1.08{col 70}{space 3}0.281{col 78}{space 4} .8104681{col 91}{space 3}  2.06392
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.012834{col 50}{space 2} .2390897{col 61}{space 1}    0.05{col 70}{space 3}0.957{col 78}{space 4} .6375913{col 91}{space 3}  1.60892
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2}  1.47375{col 50}{space 2} .3200393{col 61}{space 1}    1.79{col 70}{space 3}0.074{col 78}{space 4} .9627655{col 91}{space 3} 2.255938
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .8816561{col 50}{space 2}   .12698{col 61}{space 1}   -0.87{col 70}{space 3}0.382{col 78}{space 4} .6647648{col 91}{space 3} 1.169312
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.180469{col 50}{space 2} .1459234{col 61}{space 1}    1.34{col 70}{space 3}0.180{col 78}{space 4} .9264062{col 91}{space 3} 1.504208
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2133833{col 50}{space 2} .0551564{col 61}{space 1}   -5.98{col 70}{space 3}0.000{col 78}{space 4} .1285495{col 91}{space 3} .3542015
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 2: Improve 
. svy: logit AI_improve ib1.female_dummy if infullmodel_TableD1 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   1{txt},{res}   3962{txt}){col 67}= {res}     24.03
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}  AI_improve{col 14}{c |} Odds Ratio{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
female_dummy {c |}
{space 7}Male  {c |}{col 14}{res}{space 2} 1.807181{col 26}{space 2} .2181798{col 37}{space 1}    4.90{col 46}{space 3}0.000{col 54}{space 4} 1.426281{col 67}{space 3} 2.289802
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0742909{col 26}{space 2} .0067072{col 37}{space 1}  -28.80{col 46}{space 3}0.000{col 54}{space 4} .0622391{col 67}{space 3} .0886765
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_improve ib1.female_dummy $controls_demographics if infullmodel_TableD1 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      9.00
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                          AI_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.593788{col 50}{space 2} .2071881{col 61}{space 1}    3.59{col 70}{space 3}0.000{col 78}{space 4} 1.235216{col 91}{space 3} 2.056451
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .5859237{col 50}{space 2} .0967461{col 61}{space 1}   -3.24{col 70}{space 3}0.001{col 78}{space 4} .4238869{col 91}{space 3} .8099013
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .4313816{col 50}{space 2} .0848871{col 61}{space 1}   -4.27{col 70}{space 3}0.000{col 78}{space 4} .2932994{col 91}{space 3} .6344712
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.753261{col 50}{space 2} .2386836{col 61}{space 1}    4.12{col 70}{space 3}0.000{col 78}{space 4} 1.342552{col 91}{space 3} 2.289613
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .4766213{col 50}{space 2} .0975186{col 61}{space 1}   -3.62{col 70}{space 3}0.000{col 78}{space 4} .3191252{col 91}{space 3} .7118456
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2}  .703992{col 50}{space 2} .1022251{col 61}{space 1}   -2.42{col 70}{space 3}0.016{col 78}{space 4} .5295767{col 91}{space 3} .9358507
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .6980172{col 50}{space 2} .1996649{col 61}{space 1}   -1.26{col 70}{space 3}0.209{col 78}{space 4} .3983906{col 91}{space 3} 1.222991
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.045743{col 50}{space 2} .2381017{col 61}{space 1}    0.20{col 70}{space 3}0.844{col 78}{space 4} .6692037{col 91}{space 3} 1.634148
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.253748{col 50}{space 2} .6701505{col 61}{space 1}    0.42{col 70}{space 3}0.672{col 78}{space 4} .4396317{col 91}{space 3} 3.575457
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5114603{col 50}{space 2} .1552909{col 61}{space 1}   -2.21{col 70}{space 3}0.027{col 78}{space 4} .2820259{col 91}{space 3} .9275449
{txt}{space 34}3  {c |}{col 38}{res}{space 2}  .431203{col 50}{space 2} .1200384{col 61}{space 1}   -3.02{col 70}{space 3}0.003{col 78}{space 4} .2498341{col 91}{space 3} .7442382
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .4891384{col 50}{space 2} .1216417{col 61}{space 1}   -2.88{col 70}{space 3}0.004{col 78}{space 4}   .30039{col 91}{space 3} .7964859
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .6643646{col 50}{space 2} .1598227{col 61}{space 1}   -1.70{col 70}{space 3}0.089{col 78}{space 4} .4145481{col 91}{space 3} 1.064727
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .8602597{col 50}{space 2} .1431378{col 61}{space 1}   -0.90{col 70}{space 3}0.366{col 78}{space 4} .6208056{col 91}{space 3} 1.192075
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}  .483637{col 50}{space 2} .1091257{col 61}{space 1}   -3.22{col 70}{space 3}0.001{col 78}{space 4} .3107417{col 91}{space 3} .7527305
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .4243676{col 50}{space 2} .1168096{col 61}{space 1}   -3.11{col 70}{space 3}0.002{col 78}{space 4} .2473845{col 91}{space 3} .7279674
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .3840638{col 50}{space 2} .1048289{col 61}{space 1}   -3.51{col 70}{space 3}0.000{col 78}{space 4} .2249053{col 91}{space 3} .6558537
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2}  .296137{col 50}{space 2} .0943506{col 61}{space 1}   -3.82{col 70}{space 3}0.000{col 78}{space 4} .1585671{col 91}{space 3}   .55306
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_improve ib1.female_dummy $controls_demographics $controls_objective if infullmodel_TableD1 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      7.85
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                          AI_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.582859{col 50}{space 2} .2089079{col 61}{space 1}    3.48{col 70}{space 3}0.001{col 78}{space 4} 1.221983{col 91}{space 3} 2.050309
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .5967726{col 50}{space 2} .0987241{col 61}{space 1}   -3.12{col 70}{space 3}0.002{col 78}{space 4}  .431471{col 91}{space 3} .8254034
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .4389943{col 50}{space 2} .0868928{col 61}{space 1}   -4.16{col 70}{space 3}0.000{col 78}{space 4} .2977995{col 91}{space 3} .6471334
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.732196{col 50}{space 2} .2368375{col 61}{space 1}    4.02{col 70}{space 3}0.000{col 78}{space 4} 1.324889{col 91}{space 3} 2.264721
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .4842575{col 50}{space 2} .0992227{col 61}{space 1}   -3.54{col 70}{space 3}0.000{col 78}{space 4} .3240521{col 91}{space 3} .7236656
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .6974604{col 50}{space 2} .1028677{col 61}{space 1}   -2.44{col 70}{space 3}0.015{col 78}{space 4} .5223221{col 91}{space 3} .9313239
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .6836831{col 50}{space 2} .1929683{col 61}{space 1}   -1.35{col 70}{space 3}0.178{col 78}{space 4} .3931256{col 91}{space 3}  1.18899
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.129698{col 50}{space 2} .2598135{col 61}{space 1}    0.53{col 70}{space 3}0.596{col 78}{space 4} .7196789{col 91}{space 3} 1.773314
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.339877{col 50}{space 2} .7204718{col 61}{space 1}    0.54{col 70}{space 3}0.586{col 78}{space 4} .4668973{col 91}{space 3} 3.845108
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5083028{col 50}{space 2} .1523285{col 61}{space 1}   -2.26{col 70}{space 3}0.024{col 78}{space 4} .2824594{col 91}{space 3}  .914722
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .4113217{col 50}{space 2} .1135379{col 61}{space 1}   -3.22{col 70}{space 3}0.001{col 78}{space 4} .2394149{col 91}{space 3} .7066626
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .4648291{col 50}{space 2} .1149079{col 61}{space 1}   -3.10{col 70}{space 3}0.002{col 78}{space 4} .2862912{col 91}{space 3} .7547074
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .6062726{col 50}{space 2} .1449004{col 61}{space 1}   -2.09{col 70}{space 3}0.036{col 78}{space 4} .3794607{col 91}{space 3} .9686549
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.010877{col 50}{space 2} .1838329{col 61}{space 1}    0.06{col 70}{space 3}0.953{col 78}{space 4} .7077112{col 91}{space 3} 1.443913
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.090424{col 50}{space 2} .3315547{col 61}{space 1}    0.28{col 70}{space 3}0.776{col 78}{space 4} .6007582{col 91}{space 3} 1.979207
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .7050556{col 50}{space 2} .2294216{col 61}{space 1}   -1.07{col 70}{space 3}0.283{col 78}{space 4} .3725306{col 91}{space 3} 1.334396
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}  .859264{col 50}{space 2} .2912932{col 61}{space 1}   -0.45{col 70}{space 3}0.655{col 78}{space 4} .4420572{col 91}{space 3} 1.670224
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.600942{col 50}{space 2} .2810704{col 61}{space 1}    2.68{col 70}{space 3}0.007{col 78}{space 4} 1.134718{col 91}{space 3} 2.258723
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.005969{col 50}{space 2} .3909627{col 61}{space 1}    0.02{col 70}{space 3}0.988{col 78}{space 4} .4695373{col 91}{space 3} 2.155257
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2}  1.09556{col 50}{space 2} .4040863{col 61}{space 1}    0.25{col 70}{space 3}0.805{col 78}{space 4} .5315977{col 91}{space 3}  2.25782
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .3170697{col 50}{space 2}  .116649{col 61}{space 1}   -3.12{col 70}{space 3}0.002{col 78}{space 4} .1541361{col 91}{space 3} .6522364
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.003973{col 50}{space 2} .2001391{col 61}{space 1}    0.02{col 70}{space 3}0.984{col 78}{space 4} .6791814{col 91}{space 3} 1.484084
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2}   .88363{col 50}{space 2} .1492986{col 61}{space 1}   -0.73{col 70}{space 3}0.464{col 78}{space 4}  .634464{col 91}{space 3} 1.230648
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .1932081{col 50}{space 2} .0784027{col 61}{space 1}   -4.05{col 70}{space 3}0.000{col 78}{space 4} .0871982{col 91}{space 3} .4280981
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 3: Worsen v Improve
. svy: logit AI_directionworse ib1.female_dummy if infullmodel_TableD1 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}   1{txt},{res}   1321{txt}){col 67}= {res}     14.27
{txt}{col 49}Prob > F{col 67}= {res}    0.0002

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}  Linearized
{col 1}AI_directionworse{col 19}{c |} Odds Ratio{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}female_dummy {c |}
{space 12}Male  {c |}{col 19}{res}{space 2} .6010611{col 31}{space 2} .0809869{col 42}{space 1}   -3.78{col 51}{space 3}0.000{col 59}{space 4} .4614479{col 72}{space 3} .7829151
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 3.370245{col 31}{space 2}   .33254{col 42}{space 1}   12.31{col 51}{space 3}0.000{col 59}{space 4} 2.777135{col 72}{space 3} 4.090025
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_directionworse ib1.female_dummy $controls_demographics if infullmodel_TableD1 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}  17{txt},{res}   1305{txt}){col 67}= {res}      4.64
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                   AI_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} .7444839{col 50}{space 2} .1087284{col 61}{space 1}   -2.02{col 70}{space 3}0.044{col 78}{space 4} .5590199{col 91}{space 3} .9914786
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.630793{col 50}{space 2} .3050056{col 61}{space 1}    2.61{col 70}{space 3}0.009{col 78}{space 4} 1.129935{col 91}{space 3} 2.353663
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.731111{col 50}{space 2} .3690646{col 61}{space 1}    2.57{col 70}{space 3}0.010{col 78}{space 4} 1.139427{col 91}{space 3} 2.630047
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7325523{col 50}{space 2} .1120903{col 61}{space 1}   -2.03{col 70}{space 3}0.042{col 78}{space 4} .5425927{col 91}{space 3}  .989016
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 2.387622{col 50}{space 2} .5475305{col 61}{space 1}    3.80{col 70}{space 3}0.000{col 78}{space 4}  1.52261{col 91}{space 3} 3.744057
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.240947{col 50}{space 2} .2031517{col 61}{space 1}    1.32{col 70}{space 3}0.188{col 78}{space 4} .9000731{col 91}{space 3} 1.710916
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}  1.22502{col 50}{space 2} .4068413{col 61}{space 1}    0.61{col 70}{space 3}0.541{col 78}{space 4} .6385446{col 91}{space 3} 2.350146
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.053222{col 50}{space 2} .2908356{col 61}{space 1}    0.19{col 70}{space 3}0.851{col 78}{space 4} .6127091{col 91}{space 3} 1.810445
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .6364657{col 50}{space 2}  .396653{col 61}{space 1}   -0.72{col 70}{space 3}0.469{col 78}{space 4} .1874172{col 91}{space 3} 2.161426
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.921637{col 50}{space 2} .6822025{col 61}{space 1}    1.84{col 70}{space 3}0.066{col 78}{space 4} .9576527{col 91}{space 3} 3.855977
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 2.194025{col 50}{space 2} .7060782{col 61}{space 1}    2.44{col 70}{space 3}0.015{col 78}{space 4} 1.166965{col 91}{space 3} 4.125013
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.954344{col 50}{space 2} .5733307{col 61}{space 1}    2.28{col 70}{space 3}0.023{col 78}{space 4} 1.099161{col 91}{space 3} 3.474884
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.511639{col 50}{space 2} .4308267{col 61}{space 1}    1.45{col 70}{space 3}0.147{col 78}{space 4} .8642265{col 91}{space 3} 2.644042
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.146499{col 50}{space 2} .2142211{col 61}{space 1}    0.73{col 70}{space 3}0.464{col 78}{space 4} .7946625{col 91}{space 3} 1.654112
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 2.509639{col 50}{space 2} .6072188{col 61}{space 1}    3.80{col 70}{space 3}0.000{col 78}{space 4} 1.561242{col 91}{space 3} 4.034152
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.649087{col 50}{space 2} .5168764{col 61}{space 1}    1.60{col 70}{space 3}0.111{col 78}{space 4}  .891672{col 91}{space 3} 3.049874
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.619572{col 50}{space 2} .4988888{col 61}{space 1}    1.57{col 70}{space 3}0.118{col 78}{space 4} .8850298{col 91}{space 3} 2.963757
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .8989608{col 50}{space 2}  .320796{col 61}{space 1}   -0.30{col 70}{space 3}0.765{col 78}{space 4} .4463846{col 91}{space 3} 1.810391
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_directionworse ib1.female_dummy $controls_demographics $controls_objective if infullmodel_TableD1 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}  23{txt},{res}   1299{txt}){col 67}= {res}      4.25
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                   AI_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} .7485681{col 50}{space 2} .1106888{col 61}{space 1}   -1.96{col 70}{space 3}0.050{col 78}{space 4} .5600811{col 91}{space 3} 1.000488
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.643828{col 50}{space 2} .3076717{col 61}{space 1}    2.66{col 70}{space 3}0.008{col 78}{space 4} 1.138657{col 91}{space 3} 2.373122
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.718449{col 50}{space 2} .3686662{col 61}{space 1}    2.52{col 70}{space 3}0.012{col 78}{space 4} 1.128125{col 91}{space 3} 2.617677
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7356201{col 50}{space 2} .1132835{col 61}{space 1}   -1.99{col 70}{space 3}0.046{col 78}{space 4} .5438144{col 91}{space 3} .9950766
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 2.412895{col 50}{space 2} .5516282{col 61}{space 1}    3.85{col 70}{space 3}0.000{col 78}{space 4} 1.540852{col 91}{space 3} 3.778468
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.315665{col 50}{space 2} .2214155{col 61}{space 1}    1.63{col 70}{space 3}0.103{col 78}{space 4} .9457228{col 91}{space 3} 1.830319
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.318259{col 50}{space 2} .4386386{col 61}{space 1}    0.83{col 70}{space 3}0.406{col 78}{space 4}  .686296{col 91}{space 3} 2.532153
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2}  .926384{col 50}{space 2} .2584575{col 61}{space 1}   -0.27{col 70}{space 3}0.784{col 78}{space 4} .5359093{col 91}{space 3} 1.601367
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5966104{col 50}{space 2} .3877683{col 61}{space 1}   -0.79{col 70}{space 3}0.427{col 78}{space 4} .1667029{col 91}{space 3}   2.1352
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.823815{col 50}{space 2} .6373137{col 61}{space 1}    1.72{col 70}{space 3}0.086{col 78}{space 4} .9188914{col 91}{space 3} 3.619906
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 2.220856{col 50}{space 2} .7064391{col 61}{space 1}    2.51{col 70}{space 3}0.012{col 78}{space 4} 1.189901{col 91}{space 3} 4.145053
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.988575{col 50}{space 2} .5775212{col 61}{space 1}    2.37{col 70}{space 3}0.018{col 78}{space 4} 1.124889{col 91}{space 3} 3.515397
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.569265{col 50}{space 2} .4418033{col 61}{space 1}    1.60{col 70}{space 3}0.110{col 78}{space 4} .9033028{col 91}{space 3}  2.72621
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}  .926379{col 50}{space 2} .1941269{col 61}{space 1}   -0.36{col 70}{space 3}0.715{col 78}{space 4} .6141183{col 91}{space 3} 1.397415
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} .8738151{col 50}{space 2} .3061096{col 61}{space 1}   -0.39{col 70}{space 3}0.700{col 78}{space 4} .4394994{col 91}{space 3} 1.737324
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.107186{col 50}{space 2} .4118154{col 61}{space 1}    0.27{col 70}{space 3}0.784{col 78}{space 4} .5337372{col 91}{space 3} 2.296748
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .9733152{col 50}{space 2}   .39149{col 61}{space 1}   -0.07{col 70}{space 3}0.946{col 78}{space 4} .4421467{col 91}{space 3} 2.142597
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .7403207{col 50}{space 2} .1530683{col 61}{space 1}   -1.45{col 70}{space 3}0.146{col 78}{space 4} .4934744{col 91}{space 3} 1.110645
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.080125{col 50}{space 2} .4675268{col 61}{space 1}    0.18{col 70}{space 3}0.859{col 78}{space 4} .4620594{col 91}{space 3} 2.524936
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} .8527179{col 50}{space 2} .3655586{col 61}{space 1}   -0.37{col 70}{space 3}0.710{col 78}{space 4} .3677581{col 91}{space 3} 1.977191
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} 3.897365{col 50}{space 2}  1.55288{col 61}{space 1}    3.41{col 70}{space 3}0.001{col 78}{space 4} 1.783628{col 91}{space 3} 8.516043
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .9586039{col 50}{space 2} .2224665{col 61}{space 1}   -0.18{col 70}{space 3}0.855{col 78}{space 4} .6080186{col 91}{space 3} 1.511338
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.227994{col 50}{space 2} .2313644{col 61}{space 1}    1.09{col 70}{space 3}0.276{col 78}{space 4} .8485466{col 91}{space 3}  1.77712
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 1.157423{col 50}{space 2} .5413573{col 61}{space 1}    0.31{col 70}{space 3}0.755{col 78}{space 4} .4623788{col 91}{space 3} 2.897255
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. 
. ******* Education ********
. 
. * Model 1: Worsen 
. svy: logit AI_worsen i.degree_dummy if infullmodel_TableD1 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   1{txt},{res}   3962{txt}){col 67}= {res}     17.91
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}  Linearized
{col 1}     AI_worsen{col 16}{c |} Odds Ratio{col 28}   Std. Err.{col 40}      t{col 48}   P>|t|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}degree_dummy {c |}
Degree-Holder  {c |}{col 16}{res}{space 2} 1.408135{col 28}{space 2} .1138872{col 39}{space 1}    4.23{col 48}{space 3}0.000{col 56}{space 4} 1.201655{col 69}{space 3} 1.650094
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .2632645{col 28}{space 2} .0151077{col 39}{space 1}  -23.26{col 48}{space 3}0.000{col 56}{space 4} .2352505{col 69}{space 3} .2946146
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_worsen i.degree_dummy $controls_demographics if infullmodel_TableD1 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      4.27
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.282467{col 50}{space 2} .1163864{col 61}{space 1}    2.74{col 70}{space 3}0.006{col 78}{space 4} 1.073431{col 91}{space 3} 1.532209
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .8927627{col 50}{space 2} .1100534{col 61}{space 1}   -0.92{col 70}{space 3}0.358{col 78}{space 4} .7010902{col 91}{space 3} 1.136837
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .7758053{col 50}{space 2}  .105277{col 61}{space 1}   -1.87{col 70}{space 3}0.061{col 78}{space 4} .5945784{col 91}{space 3}  1.01227
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}  .870817{col 50}{space 2} .0765998{col 61}{space 1}   -1.57{col 70}{space 3}0.116{col 78}{space 4} .7328747{col 91}{space 3} 1.034723
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.279242{col 50}{space 2}  .134588{col 61}{space 1}    2.34{col 70}{space 3}0.019{col 78}{space 4}  1.04081{col 91}{space 3} 1.572296
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9047129{col 50}{space 2} .0865865{col 61}{space 1}   -1.05{col 70}{space 3}0.295{col 78}{space 4}   .74993{col 91}{space 3} 1.091442
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .8892559{col 50}{space 2} .1604124{col 61}{space 1}   -0.65{col 70}{space 3}0.515{col 78}{space 4} .6243557{col 91}{space 3} 1.266548
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9881668{col 50}{space 2} .1427731{col 61}{space 1}   -0.08{col 70}{space 3}0.934{col 78}{space 4} .7444041{col 91}{space 3} 1.311752
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8492572{col 50}{space 2} .2455706{col 61}{space 1}   -0.57{col 70}{space 3}0.572{col 78}{space 4} .4817605{col 91}{space 3} 1.497088
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.111529{col 50}{space 2} .1852479{col 61}{space 1}    0.63{col 70}{space 3}0.526{col 78}{space 4}  .801707{col 91}{space 3} 1.541082
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .9992331{col 50}{space 2} .1627154{col 61}{space 1}   -0.00{col 70}{space 3}0.996{col 78}{space 4} .7261303{col 91}{space 3} 1.375052
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.011691{col 50}{space 2} .1605068{col 61}{space 1}    0.07{col 70}{space 3}0.942{col 78}{space 4} .7412453{col 91}{space 3}  1.38081
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.081836{col 50}{space 2} .1754481{col 61}{space 1}    0.49{col 70}{space 3}0.628{col 78}{space 4} .7871807{col 91}{space 3} 1.486785
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.059863{col 50}{space 2} .1222794{col 61}{space 1}    0.50{col 70}{space 3}0.614{col 78}{space 4} .8453065{col 91}{space 3} 1.328878
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.498164{col 50}{space 2} .1787707{col 61}{space 1}    3.39{col 70}{space 3}0.001{col 78}{space 4} 1.185652{col 91}{space 3} 1.893046
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .6336916{col 50}{space 2} .0949955{col 61}{space 1}   -3.04{col 70}{space 3}0.002{col 78}{space 4} .4723206{col 91}{space 3} .8501958
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}  .599223{col 50}{space 2} .1024432{col 61}{space 1}   -3.00{col 70}{space 3}0.003{col 78}{space 4} .4285707{col 91}{space 3} .8378272
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .3348359{col 50}{space 2} .0651311{col 61}{space 1}   -5.62{col 70}{space 3}0.000{col 78}{space 4} .2286697{col 91}{space 3} .4902927
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_worsen i.degree_dummy $controls_demographics  $controls_objective if infullmodel_TableD1 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      4.36
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  1.26827{col 50}{space 2}  .116173{col 61}{space 1}    2.59{col 70}{space 3}0.010{col 78}{space 4} 1.059786{col 91}{space 3} 1.517768
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .9121425{col 50}{space 2}  .111979{col 61}{space 1}   -0.75{col 70}{space 3}0.454{col 78}{space 4} .7170231{col 91}{space 3} 1.160359
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .7963834{col 50}{space 2}  .107791{col 61}{space 1}   -1.68{col 70}{space 3}0.093{col 78}{space 4} .6107681{col 91}{space 3} 1.038408
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .8775447{col 50}{space 2} .0779128{col 61}{space 1}   -1.47{col 70}{space 3}0.141{col 78}{space 4} .7373476{col 91}{space 3} 1.044399
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.270618{col 50}{space 2} .1350353{col 61}{space 1}    2.25{col 70}{space 3}0.024{col 78}{space 4} 1.031634{col 91}{space 3} 1.564964
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9688247{col 50}{space 2} .0945639{col 61}{space 1}   -0.32{col 70}{space 3}0.746{col 78}{space 4} .8000861{col 91}{space 3}  1.17315
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9331544{col 50}{space 2} .1706257{col 61}{space 1}   -0.38{col 70}{space 3}0.705{col 78}{space 4} .6520264{col 91}{space 3} 1.335494
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9994992{col 50}{space 2} .1454519{col 61}{space 1}   -0.00{col 70}{space 3}0.997{col 78}{space 4} .7514043{col 91}{space 3} 1.329509
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8543309{col 50}{space 2} .2446505{col 61}{space 1}   -0.55{col 70}{space 3}0.583{col 78}{space 4} .4873009{col 91}{space 3} 1.497804
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}   1.0992{col 50}{space 2} .1850128{col 61}{space 1}    0.56{col 70}{space 3}0.574{col 78}{space 4} .7902459{col 91}{space 3} 1.528943
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.003071{col 50}{space 2} .1648735{col 61}{space 1}    0.02{col 70}{space 3}0.985{col 78}{space 4}  .726738{col 91}{space 3} 1.384476
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.023778{col 50}{space 2} .1646043{col 61}{space 1}    0.15{col 70}{space 3}0.884{col 78}{space 4} .7469762{col 91}{space 3} 1.403153
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.060449{col 50}{space 2} .1731385{col 61}{space 1}    0.36{col 70}{space 3}0.719{col 78}{space 4} .7699674{col 91}{space 3} 1.460519
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}   .94564{col 50}{space 2} .1172553{col 61}{space 1}   -0.45{col 70}{space 3}0.652{col 78}{space 4} .7415634{col 91}{space 3} 1.205878
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.000113{col 50}{space 2} .1883523{col 61}{space 1}    0.00{col 70}{space 3}1.000{col 78}{space 4} .6913415{col 91}{space 3} 1.446791
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .7392922{col 50}{space 2} .1278909{col 61}{space 1}   -1.75{col 70}{space 3}0.081{col 78}{space 4} .5266486{col 91}{space 3} 1.037794
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .8875048{col 50}{space 2} .2078441{col 61}{space 1}   -0.51{col 70}{space 3}0.610{col 78}{space 4} .5607477{col 91}{space 3} 1.404669
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.132371{col 50}{space 2} .1272744{col 61}{space 1}    1.11{col 70}{space 3}0.269{col 78}{space 4} .9084218{col 91}{space 3} 1.411529
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.293345{col 50}{space 2} .3083182{col 61}{space 1}    1.08{col 70}{space 3}0.281{col 78}{space 4} .8104681{col 91}{space 3}  2.06392
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.012834{col 50}{space 2} .2390897{col 61}{space 1}    0.05{col 70}{space 3}0.957{col 78}{space 4} .6375913{col 91}{space 3}  1.60892
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2}  1.47375{col 50}{space 2} .3200393{col 61}{space 1}    1.79{col 70}{space 3}0.074{col 78}{space 4} .9627655{col 91}{space 3} 2.255938
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .8816561{col 50}{space 2}   .12698{col 61}{space 1}   -0.87{col 70}{space 3}0.382{col 78}{space 4} .6647648{col 91}{space 3} 1.169312
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.180469{col 50}{space 2} .1459234{col 61}{space 1}    1.34{col 70}{space 3}0.180{col 78}{space 4} .9264062{col 91}{space 3} 1.504208
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2431594{col 50}{space 2} .0630194{col 61}{space 1}   -5.46{col 70}{space 3}0.000{col 78}{space 4} .1462913{col 91}{space 3} .4041697
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 2: Improve 
. svy: logit AI_improve i.degree_dummy if infullmodel_TableD1 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   1{txt},{res}   3962{txt}){col 67}= {res}     46.55
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}  Linearized
{col 1}    AI_improve{col 16}{c |} Odds Ratio{col 28}   Std. Err.{col 40}      t{col 48}   P>|t|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}degree_dummy {c |}
Degree-Holder  {c |}{col 16}{res}{space 2} 2.407881{col 28}{space 2} .3101353{col 39}{space 1}    6.82{col 48}{space 3}0.000{col 56}{space 4} 1.870539{col 69}{space 3} 3.099584
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .0643933{col 28}{space 2} .0067947{col 39}{space 1}  -25.99{col 48}{space 3}0.000{col 56}{space 4} .0523595{col 69}{space 3}  .079193
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_improve i.degree_dummy $controls_demographics if infullmodel_TableD1 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      9.00
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                          AI_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.753261{col 50}{space 2} .2386836{col 61}{space 1}    4.12{col 70}{space 3}0.000{col 78}{space 4} 1.342552{col 91}{space 3} 2.289613
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .5859237{col 50}{space 2} .0967461{col 61}{space 1}   -3.24{col 70}{space 3}0.001{col 78}{space 4} .4238869{col 91}{space 3} .8099013
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .4313816{col 50}{space 2} .0848871{col 61}{space 1}   -4.27{col 70}{space 3}0.000{col 78}{space 4} .2932994{col 91}{space 3} .6344712
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6274358{col 50}{space 2} .0815649{col 61}{space 1}   -3.59{col 70}{space 3}0.000{col 78}{space 4} .4862747{col 91}{space 3} .8095748
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .4766213{col 50}{space 2} .0975186{col 61}{space 1}   -3.62{col 70}{space 3}0.000{col 78}{space 4} .3191252{col 91}{space 3} .7118456
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2}  .703992{col 50}{space 2} .1022251{col 61}{space 1}   -2.42{col 70}{space 3}0.016{col 78}{space 4} .5295767{col 91}{space 3} .9358507
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .6980172{col 50}{space 2} .1996649{col 61}{space 1}   -1.26{col 70}{space 3}0.209{col 78}{space 4} .3983906{col 91}{space 3} 1.222991
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.045743{col 50}{space 2} .2381017{col 61}{space 1}    0.20{col 70}{space 3}0.844{col 78}{space 4} .6692037{col 91}{space 3} 1.634148
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.253748{col 50}{space 2} .6701505{col 61}{space 1}    0.42{col 70}{space 3}0.672{col 78}{space 4} .4396317{col 91}{space 3} 3.575457
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5114603{col 50}{space 2} .1552909{col 61}{space 1}   -2.21{col 70}{space 3}0.027{col 78}{space 4} .2820259{col 91}{space 3} .9275449
{txt}{space 34}3  {c |}{col 38}{res}{space 2}  .431203{col 50}{space 2} .1200384{col 61}{space 1}   -3.02{col 70}{space 3}0.003{col 78}{space 4} .2498341{col 91}{space 3} .7442382
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .4891384{col 50}{space 2} .1216417{col 61}{space 1}   -2.88{col 70}{space 3}0.004{col 78}{space 4}   .30039{col 91}{space 3} .7964859
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .6643646{col 50}{space 2} .1598227{col 61}{space 1}   -1.70{col 70}{space 3}0.089{col 78}{space 4} .4145481{col 91}{space 3} 1.064727
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .8602597{col 50}{space 2} .1431378{col 61}{space 1}   -0.90{col 70}{space 3}0.366{col 78}{space 4} .6208056{col 91}{space 3} 1.192075
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}  .483637{col 50}{space 2} .1091257{col 61}{space 1}   -3.22{col 70}{space 3}0.001{col 78}{space 4} .3107417{col 91}{space 3} .7527305
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .4243676{col 50}{space 2} .1168096{col 61}{space 1}   -3.11{col 70}{space 3}0.002{col 78}{space 4} .2473845{col 91}{space 3} .7279674
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .3840638{col 50}{space 2} .1048289{col 61}{space 1}   -3.51{col 70}{space 3}0.000{col 78}{space 4} .2249053{col 91}{space 3} .6558537
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .4719797{col 50}{space 2} .1441441{col 61}{space 1}   -2.46{col 70}{space 3}0.014{col 78}{space 4} .2593487{col 91}{space 3} .8589393
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_improve i.degree_dummy $controls_demographics $controls_objective if infullmodel_TableD1 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      7.85
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                          AI_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.732196{col 50}{space 2} .2368375{col 61}{space 1}    4.02{col 70}{space 3}0.000{col 78}{space 4} 1.324889{col 91}{space 3} 2.264721
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .5967726{col 50}{space 2} .0987241{col 61}{space 1}   -3.12{col 70}{space 3}0.002{col 78}{space 4}  .431471{col 91}{space 3} .8254034
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .4389943{col 50}{space 2} .0868928{col 61}{space 1}   -4.16{col 70}{space 3}0.000{col 78}{space 4} .2977995{col 91}{space 3} .6471334
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6317683{col 50}{space 2} .0833817{col 61}{space 1}   -3.48{col 70}{space 3}0.001{col 78}{space 4} .4877314{col 91}{space 3} .8183422
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .4842575{col 50}{space 2} .0992227{col 61}{space 1}   -3.54{col 70}{space 3}0.000{col 78}{space 4} .3240521{col 91}{space 3} .7236656
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .6974604{col 50}{space 2} .1028677{col 61}{space 1}   -2.44{col 70}{space 3}0.015{col 78}{space 4} .5223221{col 91}{space 3} .9313239
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .6836831{col 50}{space 2} .1929683{col 61}{space 1}   -1.35{col 70}{space 3}0.178{col 78}{space 4} .3931256{col 91}{space 3}  1.18899
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.129698{col 50}{space 2} .2598135{col 61}{space 1}    0.53{col 70}{space 3}0.596{col 78}{space 4} .7196789{col 91}{space 3} 1.773314
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.339877{col 50}{space 2} .7204718{col 61}{space 1}    0.54{col 70}{space 3}0.586{col 78}{space 4} .4668973{col 91}{space 3} 3.845108
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5083028{col 50}{space 2} .1523285{col 61}{space 1}   -2.26{col 70}{space 3}0.024{col 78}{space 4} .2824594{col 91}{space 3}  .914722
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .4113217{col 50}{space 2} .1135379{col 61}{space 1}   -3.22{col 70}{space 3}0.001{col 78}{space 4} .2394149{col 91}{space 3} .7066626
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .4648291{col 50}{space 2} .1149079{col 61}{space 1}   -3.10{col 70}{space 3}0.002{col 78}{space 4} .2862912{col 91}{space 3} .7547074
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .6062726{col 50}{space 2} .1449004{col 61}{space 1}   -2.09{col 70}{space 3}0.036{col 78}{space 4} .3794607{col 91}{space 3} .9686549
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.010877{col 50}{space 2} .1838329{col 61}{space 1}    0.06{col 70}{space 3}0.953{col 78}{space 4} .7077112{col 91}{space 3} 1.443913
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.090424{col 50}{space 2} .3315547{col 61}{space 1}    0.28{col 70}{space 3}0.776{col 78}{space 4} .6007582{col 91}{space 3} 1.979207
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .7050556{col 50}{space 2} .2294216{col 61}{space 1}   -1.07{col 70}{space 3}0.283{col 78}{space 4} .3725306{col 91}{space 3} 1.334396
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}  .859264{col 50}{space 2} .2912932{col 61}{space 1}   -0.45{col 70}{space 3}0.655{col 78}{space 4} .4420572{col 91}{space 3} 1.670224
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.600942{col 50}{space 2} .2810704{col 61}{space 1}    2.68{col 70}{space 3}0.007{col 78}{space 4} 1.134718{col 91}{space 3} 2.258723
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.005969{col 50}{space 2} .3909627{col 61}{space 1}    0.02{col 70}{space 3}0.988{col 78}{space 4} .4695373{col 91}{space 3} 2.155257
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2}  1.09556{col 50}{space 2} .4040863{col 61}{space 1}    0.25{col 70}{space 3}0.805{col 78}{space 4} .5315977{col 91}{space 3}  2.25782
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .3170697{col 50}{space 2}  .116649{col 61}{space 1}   -3.12{col 70}{space 3}0.002{col 78}{space 4} .1541361{col 91}{space 3} .6522364
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.003973{col 50}{space 2} .2001391{col 61}{space 1}    0.02{col 70}{space 3}0.984{col 78}{space 4} .6791814{col 91}{space 3} 1.484084
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2}   .88363{col 50}{space 2} .1492986{col 61}{space 1}   -0.73{col 70}{space 3}0.464{col 78}{space 4}  .634464{col 91}{space 3} 1.230648
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .3058212{col 50}{space 2} .1182217{col 61}{space 1}   -3.06{col 70}{space 3}0.002{col 78}{space 4} .1433233{col 91}{space 3} .6525565
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 3: Worsen v Improve
. svy: logit AI_directionworse i.degree_dummy if infullmodel_TableD1 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}   1{txt},{res}   1321{txt}){col 67}= {res}     14.25
{txt}{col 49}Prob > F{col 67}= {res}    0.0002

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}  Linearized
{col 1}AI_directionworse{col 19}{c |} Odds Ratio{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}degree_dummy {c |}
{space 3}Degree-Holder  {c |}{col 19}{res}{space 2} .5848662{col 31}{space 2}   .08311{col 42}{space 1}   -3.77{col 51}{space 3}0.000{col 59}{space 4} .4425773{col 72}{space 3} .7729013
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 3.444762{col 31}{space 2} .3945742{col 42}{space 1}   10.80{col 51}{space 3}0.000{col 59}{space 4} 2.751506{col 72}{space 3} 4.312688
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_directionworse i.degree_dummy $controls_demographics if infullmodel_TableD1 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}  17{txt},{res}   1305{txt}){col 67}= {res}      4.64
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                   AI_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7325523{col 50}{space 2} .1120903{col 61}{space 1}   -2.03{col 70}{space 3}0.042{col 78}{space 4} .5425927{col 91}{space 3}  .989016
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.630793{col 50}{space 2} .3050056{col 61}{space 1}    2.61{col 70}{space 3}0.009{col 78}{space 4} 1.129935{col 91}{space 3} 2.353663
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.731111{col 50}{space 2} .3690646{col 61}{space 1}    2.57{col 70}{space 3}0.010{col 78}{space 4} 1.139427{col 91}{space 3} 2.630047
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} 1.343212{col 50}{space 2} .1961699{col 61}{space 1}    2.02{col 70}{space 3}0.044{col 78}{space 4} 1.008595{col 91}{space 3} 1.788845
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 2.387622{col 50}{space 2} .5475305{col 61}{space 1}    3.80{col 70}{space 3}0.000{col 78}{space 4}  1.52261{col 91}{space 3} 3.744057
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.240947{col 50}{space 2} .2031517{col 61}{space 1}    1.32{col 70}{space 3}0.188{col 78}{space 4} .9000731{col 91}{space 3} 1.710916
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}  1.22502{col 50}{space 2} .4068413{col 61}{space 1}    0.61{col 70}{space 3}0.541{col 78}{space 4} .6385446{col 91}{space 3} 2.350146
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.053222{col 50}{space 2} .2908356{col 61}{space 1}    0.19{col 70}{space 3}0.851{col 78}{space 4} .6127091{col 91}{space 3} 1.810445
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .6364657{col 50}{space 2}  .396653{col 61}{space 1}   -0.72{col 70}{space 3}0.469{col 78}{space 4} .1874172{col 91}{space 3} 2.161426
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.921637{col 50}{space 2} .6822025{col 61}{space 1}    1.84{col 70}{space 3}0.066{col 78}{space 4} .9576527{col 91}{space 3} 3.855977
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 2.194025{col 50}{space 2} .7060782{col 61}{space 1}    2.44{col 70}{space 3}0.015{col 78}{space 4} 1.166965{col 91}{space 3} 4.125013
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.954344{col 50}{space 2} .5733307{col 61}{space 1}    2.28{col 70}{space 3}0.023{col 78}{space 4} 1.099161{col 91}{space 3} 3.474884
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.511639{col 50}{space 2} .4308267{col 61}{space 1}    1.45{col 70}{space 3}0.147{col 78}{space 4} .8642265{col 91}{space 3} 2.644042
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.146499{col 50}{space 2} .2142211{col 61}{space 1}    0.73{col 70}{space 3}0.464{col 78}{space 4} .7946625{col 91}{space 3} 1.654112
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 2.509639{col 50}{space 2} .6072188{col 61}{space 1}    3.80{col 70}{space 3}0.000{col 78}{space 4} 1.561242{col 91}{space 3} 4.034152
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.649087{col 50}{space 2} .5168764{col 61}{space 1}    1.60{col 70}{space 3}0.111{col 78}{space 4}  .891672{col 91}{space 3} 3.049874
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.619572{col 50}{space 2} .4988888{col 61}{space 1}    1.57{col 70}{space 3}0.118{col 78}{space 4} .8850298{col 91}{space 3} 2.963757
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .6692618{col 50}{space 2} .2259274{col 61}{space 1}   -1.19{col 70}{space 3}0.234{col 78}{space 4}  .345133{col 91}{space 3} 1.297794
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_directionworse i.degree_dummy $controls_demographics $controls_objective if infullmodel_TableD1 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}  23{txt},{res}   1299{txt}){col 67}= {res}      4.25
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                   AI_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7356201{col 50}{space 2} .1132835{col 61}{space 1}   -1.99{col 70}{space 3}0.046{col 78}{space 4} .5438144{col 91}{space 3} .9950766
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.643828{col 50}{space 2} .3076717{col 61}{space 1}    2.66{col 70}{space 3}0.008{col 78}{space 4} 1.138657{col 91}{space 3} 2.373122
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.718449{col 50}{space 2} .3686662{col 61}{space 1}    2.52{col 70}{space 3}0.012{col 78}{space 4} 1.128125{col 91}{space 3} 2.617677
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} 1.335884{col 50}{space 2} .1975336{col 61}{space 1}    1.96{col 70}{space 3}0.050{col 78}{space 4} .9995127{col 91}{space 3} 1.785456
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 2.412895{col 50}{space 2} .5516282{col 61}{space 1}    3.85{col 70}{space 3}0.000{col 78}{space 4} 1.540852{col 91}{space 3} 3.778468
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.315665{col 50}{space 2} .2214155{col 61}{space 1}    1.63{col 70}{space 3}0.103{col 78}{space 4} .9457228{col 91}{space 3} 1.830319
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.318259{col 50}{space 2} .4386386{col 61}{space 1}    0.83{col 70}{space 3}0.406{col 78}{space 4}  .686296{col 91}{space 3} 2.532153
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2}  .926384{col 50}{space 2} .2584575{col 61}{space 1}   -0.27{col 70}{space 3}0.784{col 78}{space 4} .5359093{col 91}{space 3} 1.601367
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5966104{col 50}{space 2} .3877683{col 61}{space 1}   -0.79{col 70}{space 3}0.427{col 78}{space 4} .1667029{col 91}{space 3}   2.1352
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.823815{col 50}{space 2} .6373137{col 61}{space 1}    1.72{col 70}{space 3}0.086{col 78}{space 4} .9188914{col 91}{space 3} 3.619906
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 2.220856{col 50}{space 2} .7064391{col 61}{space 1}    2.51{col 70}{space 3}0.012{col 78}{space 4} 1.189901{col 91}{space 3} 4.145053
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.988575{col 50}{space 2} .5775212{col 61}{space 1}    2.37{col 70}{space 3}0.018{col 78}{space 4} 1.124889{col 91}{space 3} 3.515397
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.569265{col 50}{space 2} .4418033{col 61}{space 1}    1.60{col 70}{space 3}0.110{col 78}{space 4} .9033028{col 91}{space 3}  2.72621
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}  .926379{col 50}{space 2} .1941269{col 61}{space 1}   -0.36{col 70}{space 3}0.715{col 78}{space 4} .6141183{col 91}{space 3} 1.397415
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} .8738151{col 50}{space 2} .3061096{col 61}{space 1}   -0.39{col 70}{space 3}0.700{col 78}{space 4} .4394994{col 91}{space 3} 1.737324
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.107186{col 50}{space 2} .4118154{col 61}{space 1}    0.27{col 70}{space 3}0.784{col 78}{space 4} .5337372{col 91}{space 3} 2.296748
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .9733152{col 50}{space 2}   .39149{col 61}{space 1}   -0.07{col 70}{space 3}0.946{col 78}{space 4} .4421467{col 91}{space 3} 2.142597
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .7403207{col 50}{space 2} .1530683{col 61}{space 1}   -1.45{col 70}{space 3}0.146{col 78}{space 4} .4934744{col 91}{space 3} 1.110645
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.080125{col 50}{space 2} .4675268{col 61}{space 1}    0.18{col 70}{space 3}0.859{col 78}{space 4} .4620594{col 91}{space 3} 2.524936
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} .8527179{col 50}{space 2} .3655586{col 61}{space 1}   -0.37{col 70}{space 3}0.710{col 78}{space 4} .3677581{col 91}{space 3} 1.977191
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} 3.897365{col 50}{space 2}  1.55288{col 61}{space 1}    3.41{col 70}{space 3}0.001{col 78}{space 4} 1.783628{col 91}{space 3} 8.516043
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .9586039{col 50}{space 2} .2224665{col 61}{space 1}   -0.18{col 70}{space 3}0.855{col 78}{space 4} .6080186{col 91}{space 3} 1.511338
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.227994{col 50}{space 2} .2313644{col 61}{space 1}    1.09{col 70}{space 3}0.276{col 78}{space 4} .8485466{col 91}{space 3}  1.77712
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .8664103{col 50}{space 2}  .380473{col 61}{space 1}   -0.33{col 70}{space 3}0.744{col 78}{space 4} .3660889{col 91}{space 3} 2.050504
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. 
. ******* ISCO-08 Occupational Group ********
. 
. * Model 1: Worsen 
. svy: logit AI_worsen i.isco08_5group if infullmodel_TableD1 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   4{txt},{res}   3959{txt}){col 67}= {res}     11.91
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                         AI_worsen{col 36}{c |} Odds Ratio{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}isco08_5group {c |}
Technician/Associate Professional  {c |}{col 36}{res}{space 2} .9987882{col 48}{space 2}  .112364{col 59}{space 1}   -0.01{col 68}{space 3}0.991{col 76}{space 4} .8010944{col 89}{space 3} 1.245269
{txt}{space 9}Clerical Support Workers  {c |}{col 36}{res}{space 2} 1.370066{col 48}{space 2} .1500356{col 59}{space 1}    2.88{col 68}{space 3}0.004{col 76}{space 4} 1.105346{col 89}{space 3} 1.698185
{txt}{space 8}Service and Sales Workers  {c |}{col 36}{res}{space 2} .5794346{col 48}{space 2} .0804415{col 59}{space 1}   -3.93{col 68}{space 3}0.000{col 76}{space 4} .4413651{col 89}{space 3} .7606955
{txt}{space 13}Manual Working Class  {c |}{col 36}{res}{space 2} .5602667{col 48}{space 2} .0862057{col 59}{space 1}   -3.77{col 68}{space 3}0.000{col 76}{space 4} .4143661{col 89}{space 3} .7575396
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .3398558{col 48}{space 2} .0213553{col 59}{space 1}  -17.18{col 68}{space 3}0.000{col 76}{space 4} .3004636{col 89}{space 3} .3844125
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_worsen i.isco08_5group $controls_demographics if infullmodel_TableD1 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      4.27
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.059863{col 50}{space 2} .1222794{col 61}{space 1}    0.50{col 70}{space 3}0.614{col 78}{space 4} .8453065{col 91}{space 3} 1.328878
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.498164{col 50}{space 2} .1787707{col 61}{space 1}    3.39{col 70}{space 3}0.001{col 78}{space 4} 1.185652{col 91}{space 3} 1.893046
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .6336916{col 50}{space 2} .0949955{col 61}{space 1}   -3.04{col 70}{space 3}0.002{col 78}{space 4} .4723206{col 91}{space 3} .8501958
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}  .599223{col 50}{space 2} .1024432{col 61}{space 1}   -3.00{col 70}{space 3}0.003{col 78}{space 4} .4285707{col 91}{space 3} .8378272
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .8927627{col 50}{space 2} .1100534{col 61}{space 1}   -0.92{col 70}{space 3}0.358{col 78}{space 4} .7010902{col 91}{space 3} 1.136837
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .7758053{col 50}{space 2}  .105277{col 61}{space 1}   -1.87{col 70}{space 3}0.061{col 78}{space 4} .5945784{col 91}{space 3}  1.01227
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}  .870817{col 50}{space 2} .0765998{col 61}{space 1}   -1.57{col 70}{space 3}0.116{col 78}{space 4} .7328747{col 91}{space 3} 1.034723
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.282467{col 50}{space 2} .1163864{col 61}{space 1}    2.74{col 70}{space 3}0.006{col 78}{space 4} 1.073431{col 91}{space 3} 1.532209
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.279242{col 50}{space 2}  .134588{col 61}{space 1}    2.34{col 70}{space 3}0.019{col 78}{space 4}  1.04081{col 91}{space 3} 1.572296
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9047129{col 50}{space 2} .0865865{col 61}{space 1}   -1.05{col 70}{space 3}0.295{col 78}{space 4}   .74993{col 91}{space 3} 1.091442
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .8892559{col 50}{space 2} .1604124{col 61}{space 1}   -0.65{col 70}{space 3}0.515{col 78}{space 4} .6243557{col 91}{space 3} 1.266548
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9881668{col 50}{space 2} .1427731{col 61}{space 1}   -0.08{col 70}{space 3}0.934{col 78}{space 4} .7444041{col 91}{space 3} 1.311752
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8492572{col 50}{space 2} .2455706{col 61}{space 1}   -0.57{col 70}{space 3}0.572{col 78}{space 4} .4817605{col 91}{space 3} 1.497088
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.111529{col 50}{space 2} .1852479{col 61}{space 1}    0.63{col 70}{space 3}0.526{col 78}{space 4}  .801707{col 91}{space 3} 1.541082
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .9992331{col 50}{space 2} .1627154{col 61}{space 1}   -0.00{col 70}{space 3}0.996{col 78}{space 4} .7261303{col 91}{space 3} 1.375052
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.011691{col 50}{space 2} .1605068{col 61}{space 1}    0.07{col 70}{space 3}0.942{col 78}{space 4} .7412453{col 91}{space 3}  1.38081
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.081836{col 50}{space 2} .1754481{col 61}{space 1}    0.49{col 70}{space 3}0.628{col 78}{space 4} .7871807{col 91}{space 3} 1.486785
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .3348359{col 50}{space 2} .0651311{col 61}{space 1}   -5.62{col 70}{space 3}0.000{col 78}{space 4} .2286697{col 91}{space 3} .4902927
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_worsen i.isco08_5group $controls_demographics $controls_objective if infullmodel_TableD1 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      4.36
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}   .94564{col 50}{space 2} .1172553{col 61}{space 1}   -0.45{col 70}{space 3}0.652{col 78}{space 4} .7415634{col 91}{space 3} 1.205878
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.000113{col 50}{space 2} .1883523{col 61}{space 1}    0.00{col 70}{space 3}1.000{col 78}{space 4} .6913415{col 91}{space 3} 1.446791
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .7392922{col 50}{space 2} .1278909{col 61}{space 1}   -1.75{col 70}{space 3}0.081{col 78}{space 4} .5266486{col 91}{space 3} 1.037794
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .8875048{col 50}{space 2} .2078441{col 61}{space 1}   -0.51{col 70}{space 3}0.610{col 78}{space 4} .5607477{col 91}{space 3} 1.404669
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .9121425{col 50}{space 2}  .111979{col 61}{space 1}   -0.75{col 70}{space 3}0.454{col 78}{space 4} .7170231{col 91}{space 3} 1.160359
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .7963834{col 50}{space 2}  .107791{col 61}{space 1}   -1.68{col 70}{space 3}0.093{col 78}{space 4} .6107681{col 91}{space 3} 1.038408
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .8775447{col 50}{space 2} .0779128{col 61}{space 1}   -1.47{col 70}{space 3}0.141{col 78}{space 4} .7373476{col 91}{space 3} 1.044399
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  1.26827{col 50}{space 2}  .116173{col 61}{space 1}    2.59{col 70}{space 3}0.010{col 78}{space 4} 1.059786{col 91}{space 3} 1.517768
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.270618{col 50}{space 2} .1350353{col 61}{space 1}    2.25{col 70}{space 3}0.024{col 78}{space 4} 1.031634{col 91}{space 3} 1.564964
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9688247{col 50}{space 2} .0945639{col 61}{space 1}   -0.32{col 70}{space 3}0.746{col 78}{space 4} .8000861{col 91}{space 3}  1.17315
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9331544{col 50}{space 2} .1706257{col 61}{space 1}   -0.38{col 70}{space 3}0.705{col 78}{space 4} .6520264{col 91}{space 3} 1.335494
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9994992{col 50}{space 2} .1454519{col 61}{space 1}   -0.00{col 70}{space 3}0.997{col 78}{space 4} .7514043{col 91}{space 3} 1.329509
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8543309{col 50}{space 2} .2446505{col 61}{space 1}   -0.55{col 70}{space 3}0.583{col 78}{space 4} .4873009{col 91}{space 3} 1.497804
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}   1.0992{col 50}{space 2} .1850128{col 61}{space 1}    0.56{col 70}{space 3}0.574{col 78}{space 4} .7902459{col 91}{space 3} 1.528943
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.003071{col 50}{space 2} .1648735{col 61}{space 1}    0.02{col 70}{space 3}0.985{col 78}{space 4}  .726738{col 91}{space 3} 1.384476
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.023778{col 50}{space 2} .1646043{col 61}{space 1}    0.15{col 70}{space 3}0.884{col 78}{space 4} .7469762{col 91}{space 3} 1.403153
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.060449{col 50}{space 2} .1731385{col 61}{space 1}    0.36{col 70}{space 3}0.719{col 78}{space 4} .7699674{col 91}{space 3} 1.460519
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.132371{col 50}{space 2} .1272744{col 61}{space 1}    1.11{col 70}{space 3}0.269{col 78}{space 4} .9084218{col 91}{space 3} 1.411529
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.293345{col 50}{space 2} .3083182{col 61}{space 1}    1.08{col 70}{space 3}0.281{col 78}{space 4} .8104681{col 91}{space 3}  2.06392
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.012834{col 50}{space 2} .2390897{col 61}{space 1}    0.05{col 70}{space 3}0.957{col 78}{space 4} .6375913{col 91}{space 3}  1.60892
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2}  1.47375{col 50}{space 2} .3200393{col 61}{space 1}    1.79{col 70}{space 3}0.074{col 78}{space 4} .9627655{col 91}{space 3} 2.255938
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .8816561{col 50}{space 2}   .12698{col 61}{space 1}   -0.87{col 70}{space 3}0.382{col 78}{space 4} .6647648{col 91}{space 3} 1.169312
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.180469{col 50}{space 2} .1459234{col 61}{space 1}    1.34{col 70}{space 3}0.180{col 78}{space 4} .9264062{col 91}{space 3} 1.504208
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2431594{col 50}{space 2} .0630194{col 61}{space 1}   -5.46{col 70}{space 3}0.000{col 78}{space 4} .1462913{col 91}{space 3} .4041697
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 2: Improve 
. svy: logit AI_improve i.isco08_5group if infullmodel_TableD1 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   4{txt},{res}   3959{txt}){col 67}= {res}     14.38
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        AI_improve{col 36}{c |} Odds Ratio{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}isco08_5group {c |}
Technician/Associate Professional  {c |}{col 36}{res}{space 2} .7417614{col 48}{space 2} .1176665{col 59}{space 1}   -1.88{col 68}{space 3}0.060{col 76}{space 4} .5434953{col 89}{space 3} 1.012355
{txt}{space 9}Clerical Support Workers  {c |}{col 36}{res}{space 2} .3316203{col 48}{space 2} .0726532{col 59}{space 1}   -5.04{col 68}{space 3}0.000{col 76}{space 4} .2158233{col 89}{space 3} .5095465
{txt}{space 8}Service and Sales Workers  {c |}{col 36}{res}{space 2}  .281905{col 48}{space 2} .0759406{col 59}{space 1}   -4.70{col 68}{space 3}0.000{col 76}{space 4} .1662391{col 89}{space 3} .4780492
{txt}{space 13}Manual Working Class  {c |}{col 36}{res}{space 2} .3041089{col 48}{space 2} .0783185{col 59}{space 1}   -4.62{col 68}{space 3}0.000{col 76}{space 4} .1835475{col 89}{space 3}   .50386
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2}  .165438{col 48}{space 2} .0127889{col 59}{space 1}  -23.27{col 68}{space 3}0.000{col 76}{space 4} .1421722{col 89}{space 3} .1925112
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_improve i.isco08_5group $controls_demographics if infullmodel_TableD1 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      9.00
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                          AI_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .8602597{col 50}{space 2} .1431378{col 61}{space 1}   -0.90{col 70}{space 3}0.366{col 78}{space 4} .6208056{col 91}{space 3} 1.192075
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}  .483637{col 50}{space 2} .1091257{col 61}{space 1}   -3.22{col 70}{space 3}0.001{col 78}{space 4} .3107417{col 91}{space 3} .7527305
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .4243676{col 50}{space 2} .1168096{col 61}{space 1}   -3.11{col 70}{space 3}0.002{col 78}{space 4} .2473845{col 91}{space 3} .7279674
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .3840638{col 50}{space 2} .1048289{col 61}{space 1}   -3.51{col 70}{space 3}0.000{col 78}{space 4} .2249053{col 91}{space 3} .6558537
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .5859237{col 50}{space 2} .0967461{col 61}{space 1}   -3.24{col 70}{space 3}0.001{col 78}{space 4} .4238869{col 91}{space 3} .8099013
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .4313816{col 50}{space 2} .0848871{col 61}{space 1}   -4.27{col 70}{space 3}0.000{col 78}{space 4} .2932994{col 91}{space 3} .6344712
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6274358{col 50}{space 2} .0815649{col 61}{space 1}   -3.59{col 70}{space 3}0.000{col 78}{space 4} .4862747{col 91}{space 3} .8095748
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.753261{col 50}{space 2} .2386836{col 61}{space 1}    4.12{col 70}{space 3}0.000{col 78}{space 4} 1.342552{col 91}{space 3} 2.289613
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .4766213{col 50}{space 2} .0975186{col 61}{space 1}   -3.62{col 70}{space 3}0.000{col 78}{space 4} .3191252{col 91}{space 3} .7118456
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2}  .703992{col 50}{space 2} .1022251{col 61}{space 1}   -2.42{col 70}{space 3}0.016{col 78}{space 4} .5295767{col 91}{space 3} .9358507
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .6980172{col 50}{space 2} .1996649{col 61}{space 1}   -1.26{col 70}{space 3}0.209{col 78}{space 4} .3983906{col 91}{space 3} 1.222991
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.045743{col 50}{space 2} .2381017{col 61}{space 1}    0.20{col 70}{space 3}0.844{col 78}{space 4} .6692037{col 91}{space 3} 1.634148
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.253748{col 50}{space 2} .6701505{col 61}{space 1}    0.42{col 70}{space 3}0.672{col 78}{space 4} .4396317{col 91}{space 3} 3.575457
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5114603{col 50}{space 2} .1552909{col 61}{space 1}   -2.21{col 70}{space 3}0.027{col 78}{space 4} .2820259{col 91}{space 3} .9275449
{txt}{space 34}3  {c |}{col 38}{res}{space 2}  .431203{col 50}{space 2} .1200384{col 61}{space 1}   -3.02{col 70}{space 3}0.003{col 78}{space 4} .2498341{col 91}{space 3} .7442382
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .4891384{col 50}{space 2} .1216417{col 61}{space 1}   -2.88{col 70}{space 3}0.004{col 78}{space 4}   .30039{col 91}{space 3} .7964859
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .6643646{col 50}{space 2} .1598227{col 61}{space 1}   -1.70{col 70}{space 3}0.089{col 78}{space 4} .4145481{col 91}{space 3} 1.064727
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .4719797{col 50}{space 2} .1441441{col 61}{space 1}   -2.46{col 70}{space 3}0.014{col 78}{space 4} .2593487{col 91}{space 3} .8589393
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_improve i.isco08_5group $controls_demographics $controls_objective if infullmodel_TableD1 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      7.85
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                          AI_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.010877{col 50}{space 2} .1838329{col 61}{space 1}    0.06{col 70}{space 3}0.953{col 78}{space 4} .7077112{col 91}{space 3} 1.443913
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.090424{col 50}{space 2} .3315547{col 61}{space 1}    0.28{col 70}{space 3}0.776{col 78}{space 4} .6007582{col 91}{space 3} 1.979207
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .7050556{col 50}{space 2} .2294216{col 61}{space 1}   -1.07{col 70}{space 3}0.283{col 78}{space 4} .3725306{col 91}{space 3} 1.334396
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}  .859264{col 50}{space 2} .2912932{col 61}{space 1}   -0.45{col 70}{space 3}0.655{col 78}{space 4} .4420572{col 91}{space 3} 1.670224
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .5967726{col 50}{space 2} .0987241{col 61}{space 1}   -3.12{col 70}{space 3}0.002{col 78}{space 4}  .431471{col 91}{space 3} .8254034
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .4389943{col 50}{space 2} .0868928{col 61}{space 1}   -4.16{col 70}{space 3}0.000{col 78}{space 4} .2977995{col 91}{space 3} .6471334
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6317683{col 50}{space 2} .0833817{col 61}{space 1}   -3.48{col 70}{space 3}0.001{col 78}{space 4} .4877314{col 91}{space 3} .8183422
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.732196{col 50}{space 2} .2368375{col 61}{space 1}    4.02{col 70}{space 3}0.000{col 78}{space 4} 1.324889{col 91}{space 3} 2.264721
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .4842575{col 50}{space 2} .0992227{col 61}{space 1}   -3.54{col 70}{space 3}0.000{col 78}{space 4} .3240521{col 91}{space 3} .7236656
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .6974604{col 50}{space 2} .1028677{col 61}{space 1}   -2.44{col 70}{space 3}0.015{col 78}{space 4} .5223221{col 91}{space 3} .9313239
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .6836831{col 50}{space 2} .1929683{col 61}{space 1}   -1.35{col 70}{space 3}0.178{col 78}{space 4} .3931256{col 91}{space 3}  1.18899
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.129698{col 50}{space 2} .2598135{col 61}{space 1}    0.53{col 70}{space 3}0.596{col 78}{space 4} .7196789{col 91}{space 3} 1.773314
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.339877{col 50}{space 2} .7204718{col 61}{space 1}    0.54{col 70}{space 3}0.586{col 78}{space 4} .4668973{col 91}{space 3} 3.845108
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5083028{col 50}{space 2} .1523285{col 61}{space 1}   -2.26{col 70}{space 3}0.024{col 78}{space 4} .2824594{col 91}{space 3}  .914722
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .4113217{col 50}{space 2} .1135379{col 61}{space 1}   -3.22{col 70}{space 3}0.001{col 78}{space 4} .2394149{col 91}{space 3} .7066626
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .4648291{col 50}{space 2} .1149079{col 61}{space 1}   -3.10{col 70}{space 3}0.002{col 78}{space 4} .2862912{col 91}{space 3} .7547074
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .6062726{col 50}{space 2} .1449004{col 61}{space 1}   -2.09{col 70}{space 3}0.036{col 78}{space 4} .3794607{col 91}{space 3} .9686549
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.600942{col 50}{space 2} .2810704{col 61}{space 1}    2.68{col 70}{space 3}0.007{col 78}{space 4} 1.134718{col 91}{space 3} 2.258723
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.005969{col 50}{space 2} .3909627{col 61}{space 1}    0.02{col 70}{space 3}0.988{col 78}{space 4} .4695373{col 91}{space 3} 2.155257
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2}  1.09556{col 50}{space 2} .4040863{col 61}{space 1}    0.25{col 70}{space 3}0.805{col 78}{space 4} .5315977{col 91}{space 3}  2.25782
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .3170697{col 50}{space 2}  .116649{col 61}{space 1}   -3.12{col 70}{space 3}0.002{col 78}{space 4} .1541361{col 91}{space 3} .6522364
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.003973{col 50}{space 2} .2001391{col 61}{space 1}    0.02{col 70}{space 3}0.984{col 78}{space 4} .6791814{col 91}{space 3} 1.484084
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2}   .88363{col 50}{space 2} .1492986{col 61}{space 1}   -0.73{col 70}{space 3}0.464{col 78}{space 4}  .634464{col 91}{space 3} 1.230648
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .3058212{col 50}{space 2} .1182217{col 61}{space 1}   -3.06{col 70}{space 3}0.002{col 78}{space 4} .1433233{col 91}{space 3} .6525565
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 3: Worsen v Improve
. svy: logit AI_directionworse i.isco08_5group if infullmodel_TableD1 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}   4{txt},{res}   1318{txt}){col 67}= {res}      8.39
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                 AI_directionworse{col 36}{c |} Odds Ratio{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}isco08_5group {c |}
Technician/Associate Professional  {c |}{col 36}{res}{space 2} 1.297547{col 48}{space 2} .2309461{col 59}{space 1}    1.46{col 68}{space 3}0.144{col 76}{space 4} .9151279{col 89}{space 3} 1.839775
{txt}{space 9}Clerical Support Workers  {c |}{col 36}{res}{space 2} 3.418554{col 48}{space 2} .7910857{col 59}{space 1}    5.31{col 68}{space 3}0.000{col 76}{space 4} 2.171129{col 89}{space 3} 5.382687
{txt}{space 8}Service and Sales Workers  {c |}{col 36}{res}{space 2} 2.066332{col 48}{space 2} .6044426{col 59}{space 1}    2.48{col 68}{space 3}0.013{col 76}{space 4} 1.164069{col 89}{space 3} 3.667935
{txt}{space 13}Manual Working Class  {c |}{col 36}{res}{space 2}  1.86877{col 48}{space 2} .5363165{col 59}{space 1}    2.18{col 68}{space 3}0.030{col 76}{space 4} 1.064256{col 89}{space 3} 3.281447
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2}  1.78686{col 48}{space 2} .1603329{col 59}{space 1}    6.47{col 68}{space 3}0.000{col 76}{space 4} 1.498453{col 89}{space 3} 2.130776
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_directionworse i.isco08_5group $controls_demographics if infullmodel_TableD1 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}  17{txt},{res}   1305{txt}){col 67}= {res}      4.64
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                   AI_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.146499{col 50}{space 2} .2142211{col 61}{space 1}    0.73{col 70}{space 3}0.464{col 78}{space 4} .7946625{col 91}{space 3} 1.654112
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 2.509639{col 50}{space 2} .6072188{col 61}{space 1}    3.80{col 70}{space 3}0.000{col 78}{space 4} 1.561242{col 91}{space 3} 4.034152
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.649087{col 50}{space 2} .5168764{col 61}{space 1}    1.60{col 70}{space 3}0.111{col 78}{space 4}  .891672{col 91}{space 3} 3.049874
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.619572{col 50}{space 2} .4988888{col 61}{space 1}    1.57{col 70}{space 3}0.118{col 78}{space 4} .8850298{col 91}{space 3} 2.963757
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.630793{col 50}{space 2} .3050056{col 61}{space 1}    2.61{col 70}{space 3}0.009{col 78}{space 4} 1.129935{col 91}{space 3} 2.353663
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.731111{col 50}{space 2} .3690646{col 61}{space 1}    2.57{col 70}{space 3}0.010{col 78}{space 4} 1.139427{col 91}{space 3} 2.630047
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} 1.343212{col 50}{space 2} .1961699{col 61}{space 1}    2.02{col 70}{space 3}0.044{col 78}{space 4} 1.008595{col 91}{space 3} 1.788845
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7325523{col 50}{space 2} .1120903{col 61}{space 1}   -2.03{col 70}{space 3}0.042{col 78}{space 4} .5425927{col 91}{space 3}  .989016
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 2.387622{col 50}{space 2} .5475305{col 61}{space 1}    3.80{col 70}{space 3}0.000{col 78}{space 4}  1.52261{col 91}{space 3} 3.744057
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.240947{col 50}{space 2} .2031517{col 61}{space 1}    1.32{col 70}{space 3}0.188{col 78}{space 4} .9000731{col 91}{space 3} 1.710916
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}  1.22502{col 50}{space 2} .4068413{col 61}{space 1}    0.61{col 70}{space 3}0.541{col 78}{space 4} .6385446{col 91}{space 3} 2.350146
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.053222{col 50}{space 2} .2908356{col 61}{space 1}    0.19{col 70}{space 3}0.851{col 78}{space 4} .6127091{col 91}{space 3} 1.810445
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .6364657{col 50}{space 2}  .396653{col 61}{space 1}   -0.72{col 70}{space 3}0.469{col 78}{space 4} .1874172{col 91}{space 3} 2.161426
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.921637{col 50}{space 2} .6822025{col 61}{space 1}    1.84{col 70}{space 3}0.066{col 78}{space 4} .9576527{col 91}{space 3} 3.855977
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 2.194025{col 50}{space 2} .7060782{col 61}{space 1}    2.44{col 70}{space 3}0.015{col 78}{space 4} 1.166965{col 91}{space 3} 4.125013
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.954344{col 50}{space 2} .5733307{col 61}{space 1}    2.28{col 70}{space 3}0.023{col 78}{space 4} 1.099161{col 91}{space 3} 3.474884
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.511639{col 50}{space 2} .4308267{col 61}{space 1}    1.45{col 70}{space 3}0.147{col 78}{space 4} .8642265{col 91}{space 3} 2.644042
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .6692618{col 50}{space 2} .2259274{col 61}{space 1}   -1.19{col 70}{space 3}0.234{col 78}{space 4}  .345133{col 91}{space 3} 1.297794
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_directionworse i.isco08_5group $controls_demographics $controls_objective if infullmodel_TableD1 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}  23{txt},{res}   1299{txt}){col 67}= {res}      4.25
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                   AI_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}  .926379{col 50}{space 2} .1941269{col 61}{space 1}   -0.36{col 70}{space 3}0.715{col 78}{space 4} .6141183{col 91}{space 3} 1.397415
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} .8738151{col 50}{space 2} .3061096{col 61}{space 1}   -0.39{col 70}{space 3}0.700{col 78}{space 4} .4394994{col 91}{space 3} 1.737324
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.107186{col 50}{space 2} .4118154{col 61}{space 1}    0.27{col 70}{space 3}0.784{col 78}{space 4} .5337372{col 91}{space 3} 2.296748
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .9733152{col 50}{space 2}   .39149{col 61}{space 1}   -0.07{col 70}{space 3}0.946{col 78}{space 4} .4421467{col 91}{space 3} 2.142597
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.643828{col 50}{space 2} .3076717{col 61}{space 1}    2.66{col 70}{space 3}0.008{col 78}{space 4} 1.138657{col 91}{space 3} 2.373122
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.718449{col 50}{space 2} .3686662{col 61}{space 1}    2.52{col 70}{space 3}0.012{col 78}{space 4} 1.128125{col 91}{space 3} 2.617677
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} 1.335884{col 50}{space 2} .1975336{col 61}{space 1}    1.96{col 70}{space 3}0.050{col 78}{space 4} .9995127{col 91}{space 3} 1.785456
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7356201{col 50}{space 2} .1132835{col 61}{space 1}   -1.99{col 70}{space 3}0.046{col 78}{space 4} .5438144{col 91}{space 3} .9950766
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 2.412895{col 50}{space 2} .5516282{col 61}{space 1}    3.85{col 70}{space 3}0.000{col 78}{space 4} 1.540852{col 91}{space 3} 3.778468
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.315665{col 50}{space 2} .2214155{col 61}{space 1}    1.63{col 70}{space 3}0.103{col 78}{space 4} .9457228{col 91}{space 3} 1.830319
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.318259{col 50}{space 2} .4386386{col 61}{space 1}    0.83{col 70}{space 3}0.406{col 78}{space 4}  .686296{col 91}{space 3} 2.532153
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2}  .926384{col 50}{space 2} .2584575{col 61}{space 1}   -0.27{col 70}{space 3}0.784{col 78}{space 4} .5359093{col 91}{space 3} 1.601367
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5966104{col 50}{space 2} .3877683{col 61}{space 1}   -0.79{col 70}{space 3}0.427{col 78}{space 4} .1667029{col 91}{space 3}   2.1352
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.823815{col 50}{space 2} .6373137{col 61}{space 1}    1.72{col 70}{space 3}0.086{col 78}{space 4} .9188914{col 91}{space 3} 3.619906
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 2.220856{col 50}{space 2} .7064391{col 61}{space 1}    2.51{col 70}{space 3}0.012{col 78}{space 4} 1.189901{col 91}{space 3} 4.145053
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.988575{col 50}{space 2} .5775212{col 61}{space 1}    2.37{col 70}{space 3}0.018{col 78}{space 4} 1.124889{col 91}{space 3} 3.515397
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.569265{col 50}{space 2} .4418033{col 61}{space 1}    1.60{col 70}{space 3}0.110{col 78}{space 4} .9033028{col 91}{space 3}  2.72621
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .7403207{col 50}{space 2} .1530683{col 61}{space 1}   -1.45{col 70}{space 3}0.146{col 78}{space 4} .4934744{col 91}{space 3} 1.110645
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.080125{col 50}{space 2} .4675268{col 61}{space 1}    0.18{col 70}{space 3}0.859{col 78}{space 4} .4620594{col 91}{space 3} 2.524936
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} .8527179{col 50}{space 2} .3655586{col 61}{space 1}   -0.37{col 70}{space 3}0.710{col 78}{space 4} .3677581{col 91}{space 3} 1.977191
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} 3.897365{col 50}{space 2}  1.55288{col 61}{space 1}    3.41{col 70}{space 3}0.001{col 78}{space 4} 1.783628{col 91}{space 3} 8.516043
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .9586039{col 50}{space 2} .2224665{col 61}{space 1}   -0.18{col 70}{space 3}0.855{col 78}{space 4} .6080186{col 91}{space 3} 1.511338
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.227994{col 50}{space 2} .2313644{col 61}{space 1}    1.09{col 70}{space 3}0.276{col 78}{space 4} .8485466{col 91}{space 3}  1.77712
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .8664103{col 50}{space 2}  .380473{col 61}{space 1}   -0.33{col 70}{space 3}0.744{col 78}{space 4} .3660889{col 91}{space 3} 2.050504
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. 
. ******* Employment Sector Group ********
. 
. * Model 1: Worsen 
. svy: logit AI_worsen i.employsector5 if infullmodel_TableD1 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   4{txt},{res}   3959{txt}){col 67}= {res}      0.24
{txt}{col 49}Prob > F{col 67}= {res}    0.9163

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9625978{col 50}{space 2} .0874099{col 61}{space 1}   -0.42{col 70}{space 3}0.675{col 78}{space 4} .8056136{col 91}{space 3} 1.150172
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9845752{col 50}{space 2} .1736077{col 61}{space 1}   -0.09{col 70}{space 3}0.930{col 78}{space 4} .6968075{col 91}{space 3} 1.391185
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9369189{col 50}{space 2} .1289741{col 61}{space 1}   -0.47{col 70}{space 3}0.636{col 78}{space 4} .7153059{col 91}{space 3} 1.227191
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2}   .78218{col 50}{space 2} .2220287{col 61}{space 1}   -0.87{col 70}{space 3}0.387{col 78}{space 4} .4483445{col 91}{space 3} 1.364588
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .3176495{col 50}{space 2} .0184933{col 61}{space 1}  -19.70{col 70}{space 3}0.000{col 78}{space 4} .2833849{col 91}{space 3} .3560571
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_worsen i.employsector5 $controls_demographics if infullmodel_TableD1 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      4.27
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9047129{col 50}{space 2} .0865865{col 61}{space 1}   -1.05{col 70}{space 3}0.295{col 78}{space 4}   .74993{col 91}{space 3} 1.091442
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .8892559{col 50}{space 2} .1604124{col 61}{space 1}   -0.65{col 70}{space 3}0.515{col 78}{space 4} .6243557{col 91}{space 3} 1.266548
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9881668{col 50}{space 2} .1427731{col 61}{space 1}   -0.08{col 70}{space 3}0.934{col 78}{space 4} .7444041{col 91}{space 3} 1.311752
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8492572{col 50}{space 2} .2455706{col 61}{space 1}   -0.57{col 70}{space 3}0.572{col 78}{space 4} .4817605{col 91}{space 3} 1.497088
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .8927627{col 50}{space 2} .1100534{col 61}{space 1}   -0.92{col 70}{space 3}0.358{col 78}{space 4} .7010902{col 91}{space 3} 1.136837
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .7758053{col 50}{space 2}  .105277{col 61}{space 1}   -1.87{col 70}{space 3}0.061{col 78}{space 4} .5945784{col 91}{space 3}  1.01227
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}  .870817{col 50}{space 2} .0765998{col 61}{space 1}   -1.57{col 70}{space 3}0.116{col 78}{space 4} .7328747{col 91}{space 3} 1.034723
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.282467{col 50}{space 2} .1163864{col 61}{space 1}    2.74{col 70}{space 3}0.006{col 78}{space 4} 1.073431{col 91}{space 3} 1.532209
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.279242{col 50}{space 2}  .134588{col 61}{space 1}    2.34{col 70}{space 3}0.019{col 78}{space 4}  1.04081{col 91}{space 3} 1.572296
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.111529{col 50}{space 2} .1852479{col 61}{space 1}    0.63{col 70}{space 3}0.526{col 78}{space 4}  .801707{col 91}{space 3} 1.541082
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .9992331{col 50}{space 2} .1627154{col 61}{space 1}   -0.00{col 70}{space 3}0.996{col 78}{space 4} .7261303{col 91}{space 3} 1.375052
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.011691{col 50}{space 2} .1605068{col 61}{space 1}    0.07{col 70}{space 3}0.942{col 78}{space 4} .7412453{col 91}{space 3}  1.38081
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.081836{col 50}{space 2} .1754481{col 61}{space 1}    0.49{col 70}{space 3}0.628{col 78}{space 4} .7871807{col 91}{space 3} 1.486785
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.059863{col 50}{space 2} .1222794{col 61}{space 1}    0.50{col 70}{space 3}0.614{col 78}{space 4} .8453065{col 91}{space 3} 1.328878
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.498164{col 50}{space 2} .1787707{col 61}{space 1}    3.39{col 70}{space 3}0.001{col 78}{space 4} 1.185652{col 91}{space 3} 1.893046
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .6336916{col 50}{space 2} .0949955{col 61}{space 1}   -3.04{col 70}{space 3}0.002{col 78}{space 4} .4723206{col 91}{space 3} .8501958
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}  .599223{col 50}{space 2} .1024432{col 61}{space 1}   -3.00{col 70}{space 3}0.003{col 78}{space 4} .4285707{col 91}{space 3} .8378272
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .3348359{col 50}{space 2} .0651311{col 61}{space 1}   -5.62{col 70}{space 3}0.000{col 78}{space 4} .2286697{col 91}{space 3} .4902927
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_worsen i.employsector5 $controls_demographics $controls_objective if infullmodel_TableD1 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      4.36
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9688247{col 50}{space 2} .0945639{col 61}{space 1}   -0.32{col 70}{space 3}0.746{col 78}{space 4} .8000861{col 91}{space 3}  1.17315
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9331544{col 50}{space 2} .1706257{col 61}{space 1}   -0.38{col 70}{space 3}0.705{col 78}{space 4} .6520264{col 91}{space 3} 1.335494
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9994992{col 50}{space 2} .1454519{col 61}{space 1}   -0.00{col 70}{space 3}0.997{col 78}{space 4} .7514043{col 91}{space 3} 1.329509
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8543309{col 50}{space 2} .2446505{col 61}{space 1}   -0.55{col 70}{space 3}0.583{col 78}{space 4} .4873009{col 91}{space 3} 1.497804
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .9121425{col 50}{space 2}  .111979{col 61}{space 1}   -0.75{col 70}{space 3}0.454{col 78}{space 4} .7170231{col 91}{space 3} 1.160359
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .7963834{col 50}{space 2}  .107791{col 61}{space 1}   -1.68{col 70}{space 3}0.093{col 78}{space 4} .6107681{col 91}{space 3} 1.038408
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .8775447{col 50}{space 2} .0779128{col 61}{space 1}   -1.47{col 70}{space 3}0.141{col 78}{space 4} .7373476{col 91}{space 3} 1.044399
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  1.26827{col 50}{space 2}  .116173{col 61}{space 1}    2.59{col 70}{space 3}0.010{col 78}{space 4} 1.059786{col 91}{space 3} 1.517768
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.270618{col 50}{space 2} .1350353{col 61}{space 1}    2.25{col 70}{space 3}0.024{col 78}{space 4} 1.031634{col 91}{space 3} 1.564964
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}   1.0992{col 50}{space 2} .1850128{col 61}{space 1}    0.56{col 70}{space 3}0.574{col 78}{space 4} .7902459{col 91}{space 3} 1.528943
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.003071{col 50}{space 2} .1648735{col 61}{space 1}    0.02{col 70}{space 3}0.985{col 78}{space 4}  .726738{col 91}{space 3} 1.384476
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.023778{col 50}{space 2} .1646043{col 61}{space 1}    0.15{col 70}{space 3}0.884{col 78}{space 4} .7469762{col 91}{space 3} 1.403153
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.060449{col 50}{space 2} .1731385{col 61}{space 1}    0.36{col 70}{space 3}0.719{col 78}{space 4} .7699674{col 91}{space 3} 1.460519
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}   .94564{col 50}{space 2} .1172553{col 61}{space 1}   -0.45{col 70}{space 3}0.652{col 78}{space 4} .7415634{col 91}{space 3} 1.205878
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.000113{col 50}{space 2} .1883523{col 61}{space 1}    0.00{col 70}{space 3}1.000{col 78}{space 4} .6913415{col 91}{space 3} 1.446791
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .7392922{col 50}{space 2} .1278909{col 61}{space 1}   -1.75{col 70}{space 3}0.081{col 78}{space 4} .5266486{col 91}{space 3} 1.037794
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .8875048{col 50}{space 2} .2078441{col 61}{space 1}   -0.51{col 70}{space 3}0.610{col 78}{space 4} .5607477{col 91}{space 3} 1.404669
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.132371{col 50}{space 2} .1272744{col 61}{space 1}    1.11{col 70}{space 3}0.269{col 78}{space 4} .9084218{col 91}{space 3} 1.411529
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.293345{col 50}{space 2} .3083182{col 61}{space 1}    1.08{col 70}{space 3}0.281{col 78}{space 4} .8104681{col 91}{space 3}  2.06392
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.012834{col 50}{space 2} .2390897{col 61}{space 1}    0.05{col 70}{space 3}0.957{col 78}{space 4} .6375913{col 91}{space 3}  1.60892
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2}  1.47375{col 50}{space 2} .3200393{col 61}{space 1}    1.79{col 70}{space 3}0.074{col 78}{space 4} .9627655{col 91}{space 3} 2.255938
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .8816561{col 50}{space 2}   .12698{col 61}{space 1}   -0.87{col 70}{space 3}0.382{col 78}{space 4} .6647648{col 91}{space 3} 1.169312
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.180469{col 50}{space 2} .1459234{col 61}{space 1}    1.34{col 70}{space 3}0.180{col 78}{space 4} .9264062{col 91}{space 3} 1.504208
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2431594{col 50}{space 2} .0630194{col 61}{space 1}   -5.46{col 70}{space 3}0.000{col 78}{space 4} .1462913{col 91}{space 3} .4041697
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 2: Improve 
. svy: logit AI_improve i.employsector5 if infullmodel_TableD1 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   4{txt},{res}   3959{txt}){col 67}= {res}      2.07
{txt}{col 49}Prob > F{col 67}= {res}    0.0824

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                          AI_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .7122379{col 50}{space 2} .0964566{col 61}{space 1}   -2.51{col 70}{space 3}0.012{col 78}{space 4} .5461523{col 91}{space 3} .9288304
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .6328008{col 50}{space 2}  .169174{col 61}{space 1}   -1.71{col 70}{space 3}0.087{col 78}{space 4} .3746588{col 91}{space 3} 1.068804
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .7599879{col 50}{space 2}  .164147{col 61}{space 1}   -1.27{col 70}{space 3}0.204{col 78}{space 4} .4976256{col 91}{space 3} 1.160675
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2}  .783538{col 50}{space 2} .3782862{col 61}{space 1}   -0.51{col 70}{space 3}0.613{col 78}{space 4}  .304075{col 91}{space 3} 2.019014
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .1215165{col 50}{space 2}  .010034{col 61}{space 1}  -25.53{col 70}{space 3}0.000{col 78}{space 4}  .103354{col 91}{space 3} .1428707
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_improve i.employsector5 $controls_demographics if infullmodel_TableD1 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      9.00
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                          AI_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2}  .703992{col 50}{space 2} .1022251{col 61}{space 1}   -2.42{col 70}{space 3}0.016{col 78}{space 4} .5295767{col 91}{space 3} .9358507
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .6980172{col 50}{space 2} .1996649{col 61}{space 1}   -1.26{col 70}{space 3}0.209{col 78}{space 4} .3983906{col 91}{space 3} 1.222991
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.045743{col 50}{space 2} .2381017{col 61}{space 1}    0.20{col 70}{space 3}0.844{col 78}{space 4} .6692037{col 91}{space 3} 1.634148
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.253748{col 50}{space 2} .6701505{col 61}{space 1}    0.42{col 70}{space 3}0.672{col 78}{space 4} .4396317{col 91}{space 3} 3.575457
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .5859237{col 50}{space 2} .0967461{col 61}{space 1}   -3.24{col 70}{space 3}0.001{col 78}{space 4} .4238869{col 91}{space 3} .8099013
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .4313816{col 50}{space 2} .0848871{col 61}{space 1}   -4.27{col 70}{space 3}0.000{col 78}{space 4} .2932994{col 91}{space 3} .6344712
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6274358{col 50}{space 2} .0815649{col 61}{space 1}   -3.59{col 70}{space 3}0.000{col 78}{space 4} .4862747{col 91}{space 3} .8095748
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.753261{col 50}{space 2} .2386836{col 61}{space 1}    4.12{col 70}{space 3}0.000{col 78}{space 4} 1.342552{col 91}{space 3} 2.289613
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .4766213{col 50}{space 2} .0975186{col 61}{space 1}   -3.62{col 70}{space 3}0.000{col 78}{space 4} .3191252{col 91}{space 3} .7118456
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5114603{col 50}{space 2} .1552909{col 61}{space 1}   -2.21{col 70}{space 3}0.027{col 78}{space 4} .2820259{col 91}{space 3} .9275449
{txt}{space 34}3  {c |}{col 38}{res}{space 2}  .431203{col 50}{space 2} .1200384{col 61}{space 1}   -3.02{col 70}{space 3}0.003{col 78}{space 4} .2498341{col 91}{space 3} .7442382
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .4891384{col 50}{space 2} .1216417{col 61}{space 1}   -2.88{col 70}{space 3}0.004{col 78}{space 4}   .30039{col 91}{space 3} .7964859
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .6643646{col 50}{space 2} .1598227{col 61}{space 1}   -1.70{col 70}{space 3}0.089{col 78}{space 4} .4145481{col 91}{space 3} 1.064727
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .8602597{col 50}{space 2} .1431378{col 61}{space 1}   -0.90{col 70}{space 3}0.366{col 78}{space 4} .6208056{col 91}{space 3} 1.192075
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}  .483637{col 50}{space 2} .1091257{col 61}{space 1}   -3.22{col 70}{space 3}0.001{col 78}{space 4} .3107417{col 91}{space 3} .7527305
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .4243676{col 50}{space 2} .1168096{col 61}{space 1}   -3.11{col 70}{space 3}0.002{col 78}{space 4} .2473845{col 91}{space 3} .7279674
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .3840638{col 50}{space 2} .1048289{col 61}{space 1}   -3.51{col 70}{space 3}0.000{col 78}{space 4} .2249053{col 91}{space 3} .6558537
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .4719797{col 50}{space 2} .1441441{col 61}{space 1}   -2.46{col 70}{space 3}0.014{col 78}{space 4} .2593487{col 91}{space 3} .8589393
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_improve i.employsector5 $controls_demographics $controls_objective if infullmodel_TableD1 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      7.85
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                          AI_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .6974604{col 50}{space 2} .1028677{col 61}{space 1}   -2.44{col 70}{space 3}0.015{col 78}{space 4} .5223221{col 91}{space 3} .9313239
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .6836831{col 50}{space 2} .1929683{col 61}{space 1}   -1.35{col 70}{space 3}0.178{col 78}{space 4} .3931256{col 91}{space 3}  1.18899
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.129698{col 50}{space 2} .2598135{col 61}{space 1}    0.53{col 70}{space 3}0.596{col 78}{space 4} .7196789{col 91}{space 3} 1.773314
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.339877{col 50}{space 2} .7204718{col 61}{space 1}    0.54{col 70}{space 3}0.586{col 78}{space 4} .4668973{col 91}{space 3} 3.845108
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .5967726{col 50}{space 2} .0987241{col 61}{space 1}   -3.12{col 70}{space 3}0.002{col 78}{space 4}  .431471{col 91}{space 3} .8254034
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .4389943{col 50}{space 2} .0868928{col 61}{space 1}   -4.16{col 70}{space 3}0.000{col 78}{space 4} .2977995{col 91}{space 3} .6471334
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6317683{col 50}{space 2} .0833817{col 61}{space 1}   -3.48{col 70}{space 3}0.001{col 78}{space 4} .4877314{col 91}{space 3} .8183422
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.732196{col 50}{space 2} .2368375{col 61}{space 1}    4.02{col 70}{space 3}0.000{col 78}{space 4} 1.324889{col 91}{space 3} 2.264721
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .4842575{col 50}{space 2} .0992227{col 61}{space 1}   -3.54{col 70}{space 3}0.000{col 78}{space 4} .3240521{col 91}{space 3} .7236656
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5083028{col 50}{space 2} .1523285{col 61}{space 1}   -2.26{col 70}{space 3}0.024{col 78}{space 4} .2824594{col 91}{space 3}  .914722
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .4113217{col 50}{space 2} .1135379{col 61}{space 1}   -3.22{col 70}{space 3}0.001{col 78}{space 4} .2394149{col 91}{space 3} .7066626
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .4648291{col 50}{space 2} .1149079{col 61}{space 1}   -3.10{col 70}{space 3}0.002{col 78}{space 4} .2862912{col 91}{space 3} .7547074
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .6062726{col 50}{space 2} .1449004{col 61}{space 1}   -2.09{col 70}{space 3}0.036{col 78}{space 4} .3794607{col 91}{space 3} .9686549
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.010877{col 50}{space 2} .1838329{col 61}{space 1}    0.06{col 70}{space 3}0.953{col 78}{space 4} .7077112{col 91}{space 3} 1.443913
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.090424{col 50}{space 2} .3315547{col 61}{space 1}    0.28{col 70}{space 3}0.776{col 78}{space 4} .6007582{col 91}{space 3} 1.979207
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .7050556{col 50}{space 2} .2294216{col 61}{space 1}   -1.07{col 70}{space 3}0.283{col 78}{space 4} .3725306{col 91}{space 3} 1.334396
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}  .859264{col 50}{space 2} .2912932{col 61}{space 1}   -0.45{col 70}{space 3}0.655{col 78}{space 4} .4420572{col 91}{space 3} 1.670224
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.600942{col 50}{space 2} .2810704{col 61}{space 1}    2.68{col 70}{space 3}0.007{col 78}{space 4} 1.134718{col 91}{space 3} 2.258723
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.005969{col 50}{space 2} .3909627{col 61}{space 1}    0.02{col 70}{space 3}0.988{col 78}{space 4} .4695373{col 91}{space 3} 2.155257
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2}  1.09556{col 50}{space 2} .4040863{col 61}{space 1}    0.25{col 70}{space 3}0.805{col 78}{space 4} .5315977{col 91}{space 3}  2.25782
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .3170697{col 50}{space 2}  .116649{col 61}{space 1}   -3.12{col 70}{space 3}0.002{col 78}{space 4} .1541361{col 91}{space 3} .6522364
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.003973{col 50}{space 2} .2001391{col 61}{space 1}    0.02{col 70}{space 3}0.984{col 78}{space 4} .6791814{col 91}{space 3} 1.484084
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2}   .88363{col 50}{space 2} .1492986{col 61}{space 1}   -0.73{col 70}{space 3}0.464{col 78}{space 4}  .634464{col 91}{space 3} 1.230648
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .3058212{col 50}{space 2} .1182217{col 61}{space 1}   -3.06{col 70}{space 3}0.002{col 78}{space 4} .1433233{col 91}{space 3} .6525565
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 3: Worsen v Improve
. svy: logit AI_directionworse i.employsector5 if infullmodel_TableD1 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}   4{txt},{res}   1318{txt}){col 67}= {res}      1.14
{txt}{col 49}Prob > F{col 67}= {res}    0.3355

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                   AI_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.321286{col 50}{space 2} .2002341{col 61}{space 1}    1.84{col 70}{space 3}0.066{col 78}{space 4} .9814845{col 91}{space 3} 1.778732
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.499574{col 50}{space 2} .4472377{col 61}{space 1}    1.36{col 70}{space 3}0.175{col 78}{space 4} .8353528{col 91}{space 3} 2.691942
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2}  1.21929{col 50}{space 2} .2905153{col 61}{space 1}    0.83{col 70}{space 3}0.405{col 78}{space 4} .7640269{col 91}{space 3} 1.945832
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.028881{col 50}{space 2} .5462679{col 61}{space 1}    0.05{col 70}{space 3}0.957{col 78}{space 4} .3630937{col 91}{space 3} 2.915489
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 2.224942{col 50}{space 2} .2068271{col 61}{space 1}    8.60{col 70}{space 3}0.000{col 78}{space 4} 1.854043{col 91}{space 3} 2.670039
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_directionworse i.employsector5 $controls_demographics if infullmodel_TableD1 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}  17{txt},{res}   1305{txt}){col 67}= {res}      4.64
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                   AI_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.240947{col 50}{space 2} .2031517{col 61}{space 1}    1.32{col 70}{space 3}0.188{col 78}{space 4} .9000731{col 91}{space 3} 1.710916
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}  1.22502{col 50}{space 2} .4068413{col 61}{space 1}    0.61{col 70}{space 3}0.541{col 78}{space 4} .6385446{col 91}{space 3} 2.350146
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.053222{col 50}{space 2} .2908356{col 61}{space 1}    0.19{col 70}{space 3}0.851{col 78}{space 4} .6127091{col 91}{space 3} 1.810445
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .6364657{col 50}{space 2}  .396653{col 61}{space 1}   -0.72{col 70}{space 3}0.469{col 78}{space 4} .1874172{col 91}{space 3} 2.161426
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.630793{col 50}{space 2} .3050056{col 61}{space 1}    2.61{col 70}{space 3}0.009{col 78}{space 4} 1.129935{col 91}{space 3} 2.353663
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.731111{col 50}{space 2} .3690646{col 61}{space 1}    2.57{col 70}{space 3}0.010{col 78}{space 4} 1.139427{col 91}{space 3} 2.630047
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} 1.343212{col 50}{space 2} .1961699{col 61}{space 1}    2.02{col 70}{space 3}0.044{col 78}{space 4} 1.008595{col 91}{space 3} 1.788845
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7325523{col 50}{space 2} .1120903{col 61}{space 1}   -2.03{col 70}{space 3}0.042{col 78}{space 4} .5425927{col 91}{space 3}  .989016
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 2.387622{col 50}{space 2} .5475305{col 61}{space 1}    3.80{col 70}{space 3}0.000{col 78}{space 4}  1.52261{col 91}{space 3} 3.744057
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.921637{col 50}{space 2} .6822025{col 61}{space 1}    1.84{col 70}{space 3}0.066{col 78}{space 4} .9576527{col 91}{space 3} 3.855977
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 2.194025{col 50}{space 2} .7060782{col 61}{space 1}    2.44{col 70}{space 3}0.015{col 78}{space 4} 1.166965{col 91}{space 3} 4.125013
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.954344{col 50}{space 2} .5733307{col 61}{space 1}    2.28{col 70}{space 3}0.023{col 78}{space 4} 1.099161{col 91}{space 3} 3.474884
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.511639{col 50}{space 2} .4308267{col 61}{space 1}    1.45{col 70}{space 3}0.147{col 78}{space 4} .8642265{col 91}{space 3} 2.644042
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.146499{col 50}{space 2} .2142211{col 61}{space 1}    0.73{col 70}{space 3}0.464{col 78}{space 4} .7946625{col 91}{space 3} 1.654112
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 2.509639{col 50}{space 2} .6072188{col 61}{space 1}    3.80{col 70}{space 3}0.000{col 78}{space 4} 1.561242{col 91}{space 3} 4.034152
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.649087{col 50}{space 2} .5168764{col 61}{space 1}    1.60{col 70}{space 3}0.111{col 78}{space 4}  .891672{col 91}{space 3} 3.049874
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.619572{col 50}{space 2} .4988888{col 61}{space 1}    1.57{col 70}{space 3}0.118{col 78}{space 4} .8850298{col 91}{space 3} 2.963757
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .6692618{col 50}{space 2} .2259274{col 61}{space 1}   -1.19{col 70}{space 3}0.234{col 78}{space 4}  .345133{col 91}{space 3} 1.297794
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_directionworse i.employsector5 $controls_demographics $controls_objective if infullmodel_TableD1 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}  23{txt},{res}   1299{txt}){col 67}= {res}      4.25
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                   AI_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.315665{col 50}{space 2} .2214155{col 61}{space 1}    1.63{col 70}{space 3}0.103{col 78}{space 4} .9457228{col 91}{space 3} 1.830319
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.318259{col 50}{space 2} .4386386{col 61}{space 1}    0.83{col 70}{space 3}0.406{col 78}{space 4}  .686296{col 91}{space 3} 2.532153
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2}  .926384{col 50}{space 2} .2584575{col 61}{space 1}   -0.27{col 70}{space 3}0.784{col 78}{space 4} .5359093{col 91}{space 3} 1.601367
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5966104{col 50}{space 2} .3877683{col 61}{space 1}   -0.79{col 70}{space 3}0.427{col 78}{space 4} .1667029{col 91}{space 3}   2.1352
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.643828{col 50}{space 2} .3076717{col 61}{space 1}    2.66{col 70}{space 3}0.008{col 78}{space 4} 1.138657{col 91}{space 3} 2.373122
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.718449{col 50}{space 2} .3686662{col 61}{space 1}    2.52{col 70}{space 3}0.012{col 78}{space 4} 1.128125{col 91}{space 3} 2.617677
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} 1.335884{col 50}{space 2} .1975336{col 61}{space 1}    1.96{col 70}{space 3}0.050{col 78}{space 4} .9995127{col 91}{space 3} 1.785456
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7356201{col 50}{space 2} .1132835{col 61}{space 1}   -1.99{col 70}{space 3}0.046{col 78}{space 4} .5438144{col 91}{space 3} .9950766
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 2.412895{col 50}{space 2} .5516282{col 61}{space 1}    3.85{col 70}{space 3}0.000{col 78}{space 4} 1.540852{col 91}{space 3} 3.778468
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.823815{col 50}{space 2} .6373137{col 61}{space 1}    1.72{col 70}{space 3}0.086{col 78}{space 4} .9188914{col 91}{space 3} 3.619906
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 2.220856{col 50}{space 2} .7064391{col 61}{space 1}    2.51{col 70}{space 3}0.012{col 78}{space 4} 1.189901{col 91}{space 3} 4.145053
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.988575{col 50}{space 2} .5775212{col 61}{space 1}    2.37{col 70}{space 3}0.018{col 78}{space 4} 1.124889{col 91}{space 3} 3.515397
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.569265{col 50}{space 2} .4418033{col 61}{space 1}    1.60{col 70}{space 3}0.110{col 78}{space 4} .9033028{col 91}{space 3}  2.72621
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}  .926379{col 50}{space 2} .1941269{col 61}{space 1}   -0.36{col 70}{space 3}0.715{col 78}{space 4} .6141183{col 91}{space 3} 1.397415
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} .8738151{col 50}{space 2} .3061096{col 61}{space 1}   -0.39{col 70}{space 3}0.700{col 78}{space 4} .4394994{col 91}{space 3} 1.737324
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.107186{col 50}{space 2} .4118154{col 61}{space 1}    0.27{col 70}{space 3}0.784{col 78}{space 4} .5337372{col 91}{space 3} 2.296748
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .9733152{col 50}{space 2}   .39149{col 61}{space 1}   -0.07{col 70}{space 3}0.946{col 78}{space 4} .4421467{col 91}{space 3} 2.142597
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .7403207{col 50}{space 2} .1530683{col 61}{space 1}   -1.45{col 70}{space 3}0.146{col 78}{space 4} .4934744{col 91}{space 3} 1.110645
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.080125{col 50}{space 2} .4675268{col 61}{space 1}    0.18{col 70}{space 3}0.859{col 78}{space 4} .4620594{col 91}{space 3} 2.524936
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} .8527179{col 50}{space 2} .3655586{col 61}{space 1}   -0.37{col 70}{space 3}0.710{col 78}{space 4} .3677581{col 91}{space 3} 1.977191
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} 3.897365{col 50}{space 2}  1.55288{col 61}{space 1}    3.41{col 70}{space 3}0.001{col 78}{space 4} 1.783628{col 91}{space 3} 8.516043
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .9586039{col 50}{space 2} .2224665{col 61}{space 1}   -0.18{col 70}{space 3}0.855{col 78}{space 4} .6080186{col 91}{space 3} 1.511338
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.227994{col 50}{space 2} .2313644{col 61}{space 1}    1.09{col 70}{space 3}0.276{col 78}{space 4} .8485466{col 91}{space 3}  1.77712
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .8664103{col 50}{space 2}  .380473{col 61}{space 1}   -0.33{col 70}{space 3}0.744{col 78}{space 4} .3660889{col 91}{space 3} 2.050504
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. 
. ******* Equivalised HH Income ********
. 
. 
. * Model 1: Worsen 
. svy: logit AI_worsen i.equivincomeHHquintile_HBAI_imp if infullmodel_TableD1 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   4{txt},{res}   3959{txt}){col 67}= {res}      1.08
{txt}{col 49}Prob > F{col 67}= {res}    0.3663

{txt}{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 32}{c |}{col 44}  Linearized
{col 1}                     AI_worsen{col 32}{c |} Odds Ratio{col 44}   Std. Err.{col 56}      t{col 64}   P>|t|{col 72}     [95% Con{col 85}f. Interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
equivincomeHHquintile_HBAI_imp {c |}
{space 28}2  {c |}{col 32}{res}{space 2} 1.175692{col 44}{space 2} .1914911{col 55}{space 1}    0.99{col 64}{space 3}0.320{col 72}{space 4} .8543025{col 85}{space 3} 1.617989
{txt}{space 28}3  {c |}{col 32}{res}{space 2} 1.117073{col 44}{space 2} .1750419{col 55}{space 1}    0.71{col 64}{space 3}0.480{col 72}{space 4} .8215994{col 85}{space 3} 1.518808
{txt}{space 28}4  {c |}{col 32}{res}{space 2} 1.129906{col 44}{space 2} .1677019{col 55}{space 1}    0.82{col 64}{space 3}0.411{col 72}{space 4} .8446321{col 85}{space 3} 1.511532
{txt}{space 17}Top Quintile  {c |}{col 32}{res}{space 2} 1.302247{col 44}{space 2} .1859645{col 55}{space 1}    1.85{col 64}{space 3}0.064{col 72}{space 4} .9842433{col 85}{space 3} 1.722997
{txt}{space 30} {c |}
{space 25}_cons {c |}{col 32}{res}{space 2} .2649718{col 44}{space 2} .0328694{col 55}{space 1}  -10.71{col 64}{space 3}0.000{col 72}{space 4} .2077671{col 85}{space 3} .3379266
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_worsen i.equivincomeHHquintile_HBAI_imp $controls_demographics if infullmodel_TableD1 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      4.27
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.111529{col 50}{space 2} .1852479{col 61}{space 1}    0.63{col 70}{space 3}0.526{col 78}{space 4}  .801707{col 91}{space 3} 1.541082
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .9992331{col 50}{space 2} .1627154{col 61}{space 1}   -0.00{col 70}{space 3}0.996{col 78}{space 4} .7261303{col 91}{space 3} 1.375052
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.011691{col 50}{space 2} .1605068{col 61}{space 1}    0.07{col 70}{space 3}0.942{col 78}{space 4} .7412453{col 91}{space 3}  1.38081
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.081836{col 50}{space 2} .1754481{col 61}{space 1}    0.49{col 70}{space 3}0.628{col 78}{space 4} .7871807{col 91}{space 3} 1.486785
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .8927627{col 50}{space 2} .1100534{col 61}{space 1}   -0.92{col 70}{space 3}0.358{col 78}{space 4} .7010902{col 91}{space 3} 1.136837
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .7758053{col 50}{space 2}  .105277{col 61}{space 1}   -1.87{col 70}{space 3}0.061{col 78}{space 4} .5945784{col 91}{space 3}  1.01227
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}  .870817{col 50}{space 2} .0765998{col 61}{space 1}   -1.57{col 70}{space 3}0.116{col 78}{space 4} .7328747{col 91}{space 3} 1.034723
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.282467{col 50}{space 2} .1163864{col 61}{space 1}    2.74{col 70}{space 3}0.006{col 78}{space 4} 1.073431{col 91}{space 3} 1.532209
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.279242{col 50}{space 2}  .134588{col 61}{space 1}    2.34{col 70}{space 3}0.019{col 78}{space 4}  1.04081{col 91}{space 3} 1.572296
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9047129{col 50}{space 2} .0865865{col 61}{space 1}   -1.05{col 70}{space 3}0.295{col 78}{space 4}   .74993{col 91}{space 3} 1.091442
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .8892559{col 50}{space 2} .1604124{col 61}{space 1}   -0.65{col 70}{space 3}0.515{col 78}{space 4} .6243557{col 91}{space 3} 1.266548
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9881668{col 50}{space 2} .1427731{col 61}{space 1}   -0.08{col 70}{space 3}0.934{col 78}{space 4} .7444041{col 91}{space 3} 1.311752
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8492572{col 50}{space 2} .2455706{col 61}{space 1}   -0.57{col 70}{space 3}0.572{col 78}{space 4} .4817605{col 91}{space 3} 1.497088
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.059863{col 50}{space 2} .1222794{col 61}{space 1}    0.50{col 70}{space 3}0.614{col 78}{space 4} .8453065{col 91}{space 3} 1.328878
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.498164{col 50}{space 2} .1787707{col 61}{space 1}    3.39{col 70}{space 3}0.001{col 78}{space 4} 1.185652{col 91}{space 3} 1.893046
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .6336916{col 50}{space 2} .0949955{col 61}{space 1}   -3.04{col 70}{space 3}0.002{col 78}{space 4} .4723206{col 91}{space 3} .8501958
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}  .599223{col 50}{space 2} .1024432{col 61}{space 1}   -3.00{col 70}{space 3}0.003{col 78}{space 4} .4285707{col 91}{space 3} .8378272
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .3348359{col 50}{space 2} .0651311{col 61}{space 1}   -5.62{col 70}{space 3}0.000{col 78}{space 4} .2286697{col 91}{space 3} .4902927
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_worsen i.equivincomeHHquintile_HBAI_imp $controls_demographics $controls_objective if infullmodel_TableD1 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      4.36
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}   1.0992{col 50}{space 2} .1850128{col 61}{space 1}    0.56{col 70}{space 3}0.574{col 78}{space 4} .7902459{col 91}{space 3} 1.528943
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.003071{col 50}{space 2} .1648735{col 61}{space 1}    0.02{col 70}{space 3}0.985{col 78}{space 4}  .726738{col 91}{space 3} 1.384476
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.023778{col 50}{space 2} .1646043{col 61}{space 1}    0.15{col 70}{space 3}0.884{col 78}{space 4} .7469762{col 91}{space 3} 1.403153
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.060449{col 50}{space 2} .1731385{col 61}{space 1}    0.36{col 70}{space 3}0.719{col 78}{space 4} .7699674{col 91}{space 3} 1.460519
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .9121425{col 50}{space 2}  .111979{col 61}{space 1}   -0.75{col 70}{space 3}0.454{col 78}{space 4} .7170231{col 91}{space 3} 1.160359
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .7963834{col 50}{space 2}  .107791{col 61}{space 1}   -1.68{col 70}{space 3}0.093{col 78}{space 4} .6107681{col 91}{space 3} 1.038408
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .8775447{col 50}{space 2} .0779128{col 61}{space 1}   -1.47{col 70}{space 3}0.141{col 78}{space 4} .7373476{col 91}{space 3} 1.044399
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  1.26827{col 50}{space 2}  .116173{col 61}{space 1}    2.59{col 70}{space 3}0.010{col 78}{space 4} 1.059786{col 91}{space 3} 1.517768
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.270618{col 50}{space 2} .1350353{col 61}{space 1}    2.25{col 70}{space 3}0.024{col 78}{space 4} 1.031634{col 91}{space 3} 1.564964
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9688247{col 50}{space 2} .0945639{col 61}{space 1}   -0.32{col 70}{space 3}0.746{col 78}{space 4} .8000861{col 91}{space 3}  1.17315
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9331544{col 50}{space 2} .1706257{col 61}{space 1}   -0.38{col 70}{space 3}0.705{col 78}{space 4} .6520264{col 91}{space 3} 1.335494
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9994992{col 50}{space 2} .1454519{col 61}{space 1}   -0.00{col 70}{space 3}0.997{col 78}{space 4} .7514043{col 91}{space 3} 1.329509
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8543309{col 50}{space 2} .2446505{col 61}{space 1}   -0.55{col 70}{space 3}0.583{col 78}{space 4} .4873009{col 91}{space 3} 1.497804
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}   .94564{col 50}{space 2} .1172553{col 61}{space 1}   -0.45{col 70}{space 3}0.652{col 78}{space 4} .7415634{col 91}{space 3} 1.205878
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.000113{col 50}{space 2} .1883523{col 61}{space 1}    0.00{col 70}{space 3}1.000{col 78}{space 4} .6913415{col 91}{space 3} 1.446791
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .7392922{col 50}{space 2} .1278909{col 61}{space 1}   -1.75{col 70}{space 3}0.081{col 78}{space 4} .5266486{col 91}{space 3} 1.037794
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .8875048{col 50}{space 2} .2078441{col 61}{space 1}   -0.51{col 70}{space 3}0.610{col 78}{space 4} .5607477{col 91}{space 3} 1.404669
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.132371{col 50}{space 2} .1272744{col 61}{space 1}    1.11{col 70}{space 3}0.269{col 78}{space 4} .9084218{col 91}{space 3} 1.411529
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.293345{col 50}{space 2} .3083182{col 61}{space 1}    1.08{col 70}{space 3}0.281{col 78}{space 4} .8104681{col 91}{space 3}  2.06392
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.012834{col 50}{space 2} .2390897{col 61}{space 1}    0.05{col 70}{space 3}0.957{col 78}{space 4} .6375913{col 91}{space 3}  1.60892
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2}  1.47375{col 50}{space 2} .3200393{col 61}{space 1}    1.79{col 70}{space 3}0.074{col 78}{space 4} .9627655{col 91}{space 3} 2.255938
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .8816561{col 50}{space 2}   .12698{col 61}{space 1}   -0.87{col 70}{space 3}0.382{col 78}{space 4} .6647648{col 91}{space 3} 1.169312
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.180469{col 50}{space 2} .1459234{col 61}{space 1}    1.34{col 70}{space 3}0.180{col 78}{space 4} .9264062{col 91}{space 3} 1.504208
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2431594{col 50}{space 2} .0630194{col 61}{space 1}   -5.46{col 70}{space 3}0.000{col 78}{space 4} .1462913{col 91}{space 3} .4041697
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 2: Improve 
. svy: logit AI_improve i.equivincomeHHquintile_HBAI_imp if infullmodel_TableD1 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   4{txt},{res}   3959{txt}){col 67}= {res}     11.26
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 32}{c |}{col 44}  Linearized
{col 1}                    AI_improve{col 32}{c |} Odds Ratio{col 44}   Std. Err.{col 56}      t{col 64}   P>|t|{col 72}     [95% Con{col 85}f. Interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
equivincomeHHquintile_HBAI_imp {c |}
{space 28}2  {c |}{col 32}{res}{space 2} .5627264{col 44}{space 2} .1606201{col 55}{space 1}   -2.01{col 64}{space 3}0.044{col 72}{space 4} .3215607{col 85}{space 3} .9847629
{txt}{space 28}3  {c |}{col 32}{res}{space 2} .6483372{col 44}{space 2} .1733206{col 55}{space 1}   -1.62{col 64}{space 3}0.105{col 72}{space 4} .3838654{col 85}{space 3} 1.095022
{txt}{space 28}4  {c |}{col 32}{res}{space 2} .8170951{col 44}{space 2} .1900892{col 55}{space 1}   -0.87{col 64}{space 3}0.385{col 72}{space 4} .5178314{col 85}{space 3} 1.289309
{txt}{space 17}Top Quintile  {c |}{col 32}{res}{space 2} 1.627773{col 44}{space 2} .3463527{col 55}{space 1}    2.29{col 64}{space 3}0.022{col 72}{space 4} 1.072561{col 85}{space 3} 2.470389
{txt}{space 30} {c |}
{space 25}_cons {c |}{col 32}{res}{space 2} .1040184{col 44}{space 2} .0202437{col 55}{space 1}  -11.63{col 64}{space 3}0.000{col 72}{space 4} .0710235{col 85}{space 3} .1523417
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_improve i.equivincomeHHquintile_HBAI_imp $controls_demographics if infullmodel_TableD1 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      9.00
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                          AI_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5114603{col 50}{space 2} .1552909{col 61}{space 1}   -2.21{col 70}{space 3}0.027{col 78}{space 4} .2820259{col 91}{space 3} .9275449
{txt}{space 34}3  {c |}{col 38}{res}{space 2}  .431203{col 50}{space 2} .1200384{col 61}{space 1}   -3.02{col 70}{space 3}0.003{col 78}{space 4} .2498341{col 91}{space 3} .7442382
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .4891384{col 50}{space 2} .1216417{col 61}{space 1}   -2.88{col 70}{space 3}0.004{col 78}{space 4}   .30039{col 91}{space 3} .7964859
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .6643646{col 50}{space 2} .1598227{col 61}{space 1}   -1.70{col 70}{space 3}0.089{col 78}{space 4} .4145481{col 91}{space 3} 1.064727
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .5859237{col 50}{space 2} .0967461{col 61}{space 1}   -3.24{col 70}{space 3}0.001{col 78}{space 4} .4238869{col 91}{space 3} .8099013
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .4313816{col 50}{space 2} .0848871{col 61}{space 1}   -4.27{col 70}{space 3}0.000{col 78}{space 4} .2932994{col 91}{space 3} .6344712
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6274358{col 50}{space 2} .0815649{col 61}{space 1}   -3.59{col 70}{space 3}0.000{col 78}{space 4} .4862747{col 91}{space 3} .8095748
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.753261{col 50}{space 2} .2386836{col 61}{space 1}    4.12{col 70}{space 3}0.000{col 78}{space 4} 1.342552{col 91}{space 3} 2.289613
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .4766213{col 50}{space 2} .0975186{col 61}{space 1}   -3.62{col 70}{space 3}0.000{col 78}{space 4} .3191252{col 91}{space 3} .7118456
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2}  .703992{col 50}{space 2} .1022251{col 61}{space 1}   -2.42{col 70}{space 3}0.016{col 78}{space 4} .5295767{col 91}{space 3} .9358507
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .6980172{col 50}{space 2} .1996649{col 61}{space 1}   -1.26{col 70}{space 3}0.209{col 78}{space 4} .3983906{col 91}{space 3} 1.222991
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.045743{col 50}{space 2} .2381017{col 61}{space 1}    0.20{col 70}{space 3}0.844{col 78}{space 4} .6692037{col 91}{space 3} 1.634148
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.253748{col 50}{space 2} .6701505{col 61}{space 1}    0.42{col 70}{space 3}0.672{col 78}{space 4} .4396317{col 91}{space 3} 3.575457
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .8602597{col 50}{space 2} .1431378{col 61}{space 1}   -0.90{col 70}{space 3}0.366{col 78}{space 4} .6208056{col 91}{space 3} 1.192075
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}  .483637{col 50}{space 2} .1091257{col 61}{space 1}   -3.22{col 70}{space 3}0.001{col 78}{space 4} .3107417{col 91}{space 3} .7527305
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .4243676{col 50}{space 2} .1168096{col 61}{space 1}   -3.11{col 70}{space 3}0.002{col 78}{space 4} .2473845{col 91}{space 3} .7279674
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .3840638{col 50}{space 2} .1048289{col 61}{space 1}   -3.51{col 70}{space 3}0.000{col 78}{space 4} .2249053{col 91}{space 3} .6558537
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .4719797{col 50}{space 2} .1441441{col 61}{space 1}   -2.46{col 70}{space 3}0.014{col 78}{space 4} .2593487{col 91}{space 3} .8589393
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_improve i.equivincomeHHquintile_HBAI_imp $controls_demographics $controls_objective if infullmodel_TableD1 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      7.85
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                          AI_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5083028{col 50}{space 2} .1523285{col 61}{space 1}   -2.26{col 70}{space 3}0.024{col 78}{space 4} .2824594{col 91}{space 3}  .914722
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .4113217{col 50}{space 2} .1135379{col 61}{space 1}   -3.22{col 70}{space 3}0.001{col 78}{space 4} .2394149{col 91}{space 3} .7066626
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .4648291{col 50}{space 2} .1149079{col 61}{space 1}   -3.10{col 70}{space 3}0.002{col 78}{space 4} .2862912{col 91}{space 3} .7547074
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .6062726{col 50}{space 2} .1449004{col 61}{space 1}   -2.09{col 70}{space 3}0.036{col 78}{space 4} .3794607{col 91}{space 3} .9686549
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .5967726{col 50}{space 2} .0987241{col 61}{space 1}   -3.12{col 70}{space 3}0.002{col 78}{space 4}  .431471{col 91}{space 3} .8254034
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .4389943{col 50}{space 2} .0868928{col 61}{space 1}   -4.16{col 70}{space 3}0.000{col 78}{space 4} .2977995{col 91}{space 3} .6471334
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6317683{col 50}{space 2} .0833817{col 61}{space 1}   -3.48{col 70}{space 3}0.001{col 78}{space 4} .4877314{col 91}{space 3} .8183422
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.732196{col 50}{space 2} .2368375{col 61}{space 1}    4.02{col 70}{space 3}0.000{col 78}{space 4} 1.324889{col 91}{space 3} 2.264721
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .4842575{col 50}{space 2} .0992227{col 61}{space 1}   -3.54{col 70}{space 3}0.000{col 78}{space 4} .3240521{col 91}{space 3} .7236656
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .6974604{col 50}{space 2} .1028677{col 61}{space 1}   -2.44{col 70}{space 3}0.015{col 78}{space 4} .5223221{col 91}{space 3} .9313239
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .6836831{col 50}{space 2} .1929683{col 61}{space 1}   -1.35{col 70}{space 3}0.178{col 78}{space 4} .3931256{col 91}{space 3}  1.18899
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.129698{col 50}{space 2} .2598135{col 61}{space 1}    0.53{col 70}{space 3}0.596{col 78}{space 4} .7196789{col 91}{space 3} 1.773314
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.339877{col 50}{space 2} .7204718{col 61}{space 1}    0.54{col 70}{space 3}0.586{col 78}{space 4} .4668973{col 91}{space 3} 3.845108
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.010877{col 50}{space 2} .1838329{col 61}{space 1}    0.06{col 70}{space 3}0.953{col 78}{space 4} .7077112{col 91}{space 3} 1.443913
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.090424{col 50}{space 2} .3315547{col 61}{space 1}    0.28{col 70}{space 3}0.776{col 78}{space 4} .6007582{col 91}{space 3} 1.979207
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .7050556{col 50}{space 2} .2294216{col 61}{space 1}   -1.07{col 70}{space 3}0.283{col 78}{space 4} .3725306{col 91}{space 3} 1.334396
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}  .859264{col 50}{space 2} .2912932{col 61}{space 1}   -0.45{col 70}{space 3}0.655{col 78}{space 4} .4420572{col 91}{space 3} 1.670224
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.600942{col 50}{space 2} .2810704{col 61}{space 1}    2.68{col 70}{space 3}0.007{col 78}{space 4} 1.134718{col 91}{space 3} 2.258723
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.005969{col 50}{space 2} .3909627{col 61}{space 1}    0.02{col 70}{space 3}0.988{col 78}{space 4} .4695373{col 91}{space 3} 2.155257
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2}  1.09556{col 50}{space 2} .4040863{col 61}{space 1}    0.25{col 70}{space 3}0.805{col 78}{space 4} .5315977{col 91}{space 3}  2.25782
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .3170697{col 50}{space 2}  .116649{col 61}{space 1}   -3.12{col 70}{space 3}0.002{col 78}{space 4} .1541361{col 91}{space 3} .6522364
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.003973{col 50}{space 2} .2001391{col 61}{space 1}    0.02{col 70}{space 3}0.984{col 78}{space 4} .6791814{col 91}{space 3} 1.484084
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2}   .88363{col 50}{space 2} .1492986{col 61}{space 1}   -0.73{col 70}{space 3}0.464{col 78}{space 4}  .634464{col 91}{space 3} 1.230648
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .3058212{col 50}{space 2} .1182217{col 61}{space 1}   -3.06{col 70}{space 3}0.002{col 78}{space 4} .1433233{col 91}{space 3} .6525565
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 3: Worsen v Improve
. svy: logit AI_directionworse i.equivincomeHHquintile_HBAI_imp if infullmodel_TableD1 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}   4{txt},{res}   1318{txt}){col 67}= {res}      5.61
{txt}{col 49}Prob > F{col 67}= {res}    0.0002

{txt}{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 32}{c |}{col 44}  Linearized
{col 1}             AI_directionworse{col 32}{c |} Odds Ratio{col 44}   Std. Err.{col 56}      t{col 64}   P>|t|{col 72}     [95% Con{col 85}f. Interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
equivincomeHHquintile_HBAI_imp {c |}
{space 28}2  {c |}{col 32}{res}{space 2} 1.932096{col 44}{space 2} .6015029{col 55}{space 1}    2.12{col 64}{space 3}0.035{col 72}{space 4} 1.049031{col 85}{space 3} 3.558517
{txt}{space 28}3  {c |}{col 32}{res}{space 2} 1.626019{col 44}{space 2} .4759396{col 55}{space 1}    1.66{col 64}{space 3}0.097{col 72}{space 4} .9156899{col 85}{space 3} 2.887372
{txt}{space 28}4  {c |}{col 32}{res}{space 2} 1.323002{col 44}{space 2} .3419835{col 55}{space 1}    1.08{col 64}{space 3}0.279{col 72}{space 4}  .796767{col 85}{space 3} 2.196797
{txt}{space 17}Top Quintile  {c |}{col 32}{res}{space 2} .7968853{col 44}{space 2} .1901129{col 55}{space 1}   -0.95{col 64}{space 3}0.341{col 72}{space 4} .4990436{col 85}{space 3} 1.272486
{txt}{space 30} {c |}
{space 25}_cons {c |}{col 32}{res}{space 2} 2.223232{col 44}{space 2} .4814285{col 55}{space 1}    3.69{col 64}{space 3}0.000{col 72}{space 4}  1.45376{col 85}{space 3} 3.399983
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_directionworse i.equivincomeHHquintile_HBAI_imp $controls_demographics if infullmodel_TableD1 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}  17{txt},{res}   1305{txt}){col 67}= {res}      4.64
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                   AI_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.921637{col 50}{space 2} .6822025{col 61}{space 1}    1.84{col 70}{space 3}0.066{col 78}{space 4} .9576527{col 91}{space 3} 3.855977
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 2.194025{col 50}{space 2} .7060782{col 61}{space 1}    2.44{col 70}{space 3}0.015{col 78}{space 4} 1.166965{col 91}{space 3} 4.125013
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.954344{col 50}{space 2} .5733307{col 61}{space 1}    2.28{col 70}{space 3}0.023{col 78}{space 4} 1.099161{col 91}{space 3} 3.474884
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.511639{col 50}{space 2} .4308267{col 61}{space 1}    1.45{col 70}{space 3}0.147{col 78}{space 4} .8642265{col 91}{space 3} 2.644042
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.630793{col 50}{space 2} .3050056{col 61}{space 1}    2.61{col 70}{space 3}0.009{col 78}{space 4} 1.129935{col 91}{space 3} 2.353663
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.731111{col 50}{space 2} .3690646{col 61}{space 1}    2.57{col 70}{space 3}0.010{col 78}{space 4} 1.139427{col 91}{space 3} 2.630047
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} 1.343212{col 50}{space 2} .1961699{col 61}{space 1}    2.02{col 70}{space 3}0.044{col 78}{space 4} 1.008595{col 91}{space 3} 1.788845
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7325523{col 50}{space 2} .1120903{col 61}{space 1}   -2.03{col 70}{space 3}0.042{col 78}{space 4} .5425927{col 91}{space 3}  .989016
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 2.387622{col 50}{space 2} .5475305{col 61}{space 1}    3.80{col 70}{space 3}0.000{col 78}{space 4}  1.52261{col 91}{space 3} 3.744057
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.240947{col 50}{space 2} .2031517{col 61}{space 1}    1.32{col 70}{space 3}0.188{col 78}{space 4} .9000731{col 91}{space 3} 1.710916
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}  1.22502{col 50}{space 2} .4068413{col 61}{space 1}    0.61{col 70}{space 3}0.541{col 78}{space 4} .6385446{col 91}{space 3} 2.350146
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.053222{col 50}{space 2} .2908356{col 61}{space 1}    0.19{col 70}{space 3}0.851{col 78}{space 4} .6127091{col 91}{space 3} 1.810445
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .6364657{col 50}{space 2}  .396653{col 61}{space 1}   -0.72{col 70}{space 3}0.469{col 78}{space 4} .1874172{col 91}{space 3} 2.161426
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.146499{col 50}{space 2} .2142211{col 61}{space 1}    0.73{col 70}{space 3}0.464{col 78}{space 4} .7946625{col 91}{space 3} 1.654112
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 2.509639{col 50}{space 2} .6072188{col 61}{space 1}    3.80{col 70}{space 3}0.000{col 78}{space 4} 1.561242{col 91}{space 3} 4.034152
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.649087{col 50}{space 2} .5168764{col 61}{space 1}    1.60{col 70}{space 3}0.111{col 78}{space 4}  .891672{col 91}{space 3} 3.049874
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.619572{col 50}{space 2} .4988888{col 61}{space 1}    1.57{col 70}{space 3}0.118{col 78}{space 4} .8850298{col 91}{space 3} 2.963757
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .6692618{col 50}{space 2} .2259274{col 61}{space 1}   -1.19{col 70}{space 3}0.234{col 78}{space 4}  .345133{col 91}{space 3} 1.297794
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_directionworse i.equivincomeHHquintile_HBAI_imp $controls_demographics $controls_objective if infullmodel_TableD1 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}  23{txt},{res}   1299{txt}){col 67}= {res}      4.25
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                   AI_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.823815{col 50}{space 2} .6373137{col 61}{space 1}    1.72{col 70}{space 3}0.086{col 78}{space 4} .9188914{col 91}{space 3} 3.619906
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 2.220856{col 50}{space 2} .7064391{col 61}{space 1}    2.51{col 70}{space 3}0.012{col 78}{space 4} 1.189901{col 91}{space 3} 4.145053
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.988575{col 50}{space 2} .5775212{col 61}{space 1}    2.37{col 70}{space 3}0.018{col 78}{space 4} 1.124889{col 91}{space 3} 3.515397
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.569265{col 50}{space 2} .4418033{col 61}{space 1}    1.60{col 70}{space 3}0.110{col 78}{space 4} .9033028{col 91}{space 3}  2.72621
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.643828{col 50}{space 2} .3076717{col 61}{space 1}    2.66{col 70}{space 3}0.008{col 78}{space 4} 1.138657{col 91}{space 3} 2.373122
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.718449{col 50}{space 2} .3686662{col 61}{space 1}    2.52{col 70}{space 3}0.012{col 78}{space 4} 1.128125{col 91}{space 3} 2.617677
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} 1.335884{col 50}{space 2} .1975336{col 61}{space 1}    1.96{col 70}{space 3}0.050{col 78}{space 4} .9995127{col 91}{space 3} 1.785456
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7356201{col 50}{space 2} .1132835{col 61}{space 1}   -1.99{col 70}{space 3}0.046{col 78}{space 4} .5438144{col 91}{space 3} .9950766
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 2.412895{col 50}{space 2} .5516282{col 61}{space 1}    3.85{col 70}{space 3}0.000{col 78}{space 4} 1.540852{col 91}{space 3} 3.778468
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.315665{col 50}{space 2} .2214155{col 61}{space 1}    1.63{col 70}{space 3}0.103{col 78}{space 4} .9457228{col 91}{space 3} 1.830319
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.318259{col 50}{space 2} .4386386{col 61}{space 1}    0.83{col 70}{space 3}0.406{col 78}{space 4}  .686296{col 91}{space 3} 2.532153
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2}  .926384{col 50}{space 2} .2584575{col 61}{space 1}   -0.27{col 70}{space 3}0.784{col 78}{space 4} .5359093{col 91}{space 3} 1.601367
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5966104{col 50}{space 2} .3877683{col 61}{space 1}   -0.79{col 70}{space 3}0.427{col 78}{space 4} .1667029{col 91}{space 3}   2.1352
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}  .926379{col 50}{space 2} .1941269{col 61}{space 1}   -0.36{col 70}{space 3}0.715{col 78}{space 4} .6141183{col 91}{space 3} 1.397415
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} .8738151{col 50}{space 2} .3061096{col 61}{space 1}   -0.39{col 70}{space 3}0.700{col 78}{space 4} .4394994{col 91}{space 3} 1.737324
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.107186{col 50}{space 2} .4118154{col 61}{space 1}    0.27{col 70}{space 3}0.784{col 78}{space 4} .5337372{col 91}{space 3} 2.296748
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .9733152{col 50}{space 2}   .39149{col 61}{space 1}   -0.07{col 70}{space 3}0.946{col 78}{space 4} .4421467{col 91}{space 3} 2.142597
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .7403207{col 50}{space 2} .1530683{col 61}{space 1}   -1.45{col 70}{space 3}0.146{col 78}{space 4} .4934744{col 91}{space 3} 1.110645
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.080125{col 50}{space 2} .4675268{col 61}{space 1}    0.18{col 70}{space 3}0.859{col 78}{space 4} .4620594{col 91}{space 3} 2.524936
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} .8527179{col 50}{space 2} .3655586{col 61}{space 1}   -0.37{col 70}{space 3}0.710{col 78}{space 4} .3677581{col 91}{space 3} 1.977191
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} 3.897365{col 50}{space 2}  1.55288{col 61}{space 1}    3.41{col 70}{space 3}0.001{col 78}{space 4} 1.783628{col 91}{space 3} 8.516043
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .9586039{col 50}{space 2} .2224665{col 61}{space 1}   -0.18{col 70}{space 3}0.855{col 78}{space 4} .6080186{col 91}{space 3} 1.511338
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.227994{col 50}{space 2} .2313644{col 61}{space 1}    1.09{col 70}{space 3}0.276{col 78}{space 4} .8485466{col 91}{space 3}  1.77712
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .8664103{col 50}{space 2}  .380473{col 61}{space 1}   -0.33{col 70}{space 3}0.744{col 78}{space 4} .3660889{col 91}{space 3} 2.050504
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. ******* Employment Status ********
. 
. 
. * Model 1: Worsen 
. svy: logit AI_worsen ib2.employmentstatus5 if infullmodel_TableD1 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   1{txt},{res}   3962{txt}){col 67}= {res}      0.65
{txt}{col 49}Prob > F{col 67}= {res}    0.4196

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}  Linearized
{col 1}        AI_worsen{col 19}{c |} Odds Ratio{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
employmentstatus5 {c |}
{space 7}FT Worker  {c |}{col 19}{res}{space 2} .9283357{col 31}{space 2} .0855293{col 42}{space 1}   -0.81{col 51}{space 3}0.420{col 59}{space 4} .7749226{col 72}{space 3}  1.11212
{txt}{space 12}_cons {c |}{col 19}{res}{space 2}   .32743{col 31}{space 2} .0259688{col 42}{space 1}  -14.08{col 51}{space 3}0.000{col 59}{space 4} .2802774{col 72}{space 3} .3825152
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_worsen ib2.employmentstatus5 $controls_demographics if infullmodel_TableD1 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      4.27
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} .7817127{col 50}{space 2} .0822433{col 61}{space 1}   -2.34{col 70}{space 3}0.019{col 78}{space 4} .6360124{col 91}{space 3} .9607906
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .8927627{col 50}{space 2} .1100534{col 61}{space 1}   -0.92{col 70}{space 3}0.358{col 78}{space 4} .7010902{col 91}{space 3} 1.136837
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .7758053{col 50}{space 2}  .105277{col 61}{space 1}   -1.87{col 70}{space 3}0.061{col 78}{space 4} .5945784{col 91}{space 3}  1.01227
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}  .870817{col 50}{space 2} .0765998{col 61}{space 1}   -1.57{col 70}{space 3}0.116{col 78}{space 4} .7328747{col 91}{space 3} 1.034723
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.282467{col 50}{space 2} .1163864{col 61}{space 1}    2.74{col 70}{space 3}0.006{col 78}{space 4} 1.073431{col 91}{space 3} 1.532209
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9047129{col 50}{space 2} .0865865{col 61}{space 1}   -1.05{col 70}{space 3}0.295{col 78}{space 4}   .74993{col 91}{space 3} 1.091442
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .8892559{col 50}{space 2} .1604124{col 61}{space 1}   -0.65{col 70}{space 3}0.515{col 78}{space 4} .6243557{col 91}{space 3} 1.266548
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9881668{col 50}{space 2} .1427731{col 61}{space 1}   -0.08{col 70}{space 3}0.934{col 78}{space 4} .7444041{col 91}{space 3} 1.311752
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8492572{col 50}{space 2} .2455706{col 61}{space 1}   -0.57{col 70}{space 3}0.572{col 78}{space 4} .4817605{col 91}{space 3} 1.497088
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.111529{col 50}{space 2} .1852479{col 61}{space 1}    0.63{col 70}{space 3}0.526{col 78}{space 4}  .801707{col 91}{space 3} 1.541082
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .9992331{col 50}{space 2} .1627154{col 61}{space 1}   -0.00{col 70}{space 3}0.996{col 78}{space 4} .7261303{col 91}{space 3} 1.375052
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.011691{col 50}{space 2} .1605068{col 61}{space 1}    0.07{col 70}{space 3}0.942{col 78}{space 4} .7412453{col 91}{space 3}  1.38081
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.081836{col 50}{space 2} .1754481{col 61}{space 1}    0.49{col 70}{space 3}0.628{col 78}{space 4} .7871807{col 91}{space 3} 1.486785
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.059863{col 50}{space 2} .1222794{col 61}{space 1}    0.50{col 70}{space 3}0.614{col 78}{space 4} .8453065{col 91}{space 3} 1.328878
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.498164{col 50}{space 2} .1787707{col 61}{space 1}    3.39{col 70}{space 3}0.001{col 78}{space 4} 1.185652{col 91}{space 3} 1.893046
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .6336916{col 50}{space 2} .0949955{col 61}{space 1}   -3.04{col 70}{space 3}0.002{col 78}{space 4} .4723206{col 91}{space 3} .8501958
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}  .599223{col 50}{space 2} .1024432{col 61}{space 1}   -3.00{col 70}{space 3}0.003{col 78}{space 4} .4285707{col 91}{space 3} .8378272
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .4283363{col 50}{space 2}  .091154{col 61}{space 1}   -3.98{col 70}{space 3}0.000{col 78}{space 4} .2822188{col 91}{space 3} .6501055
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_worsen ib2.employmentstatus5 $controls_demographics $controls_objective if infullmodel_TableD1 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      4.36
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                           AI_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} .7870184{col 50}{space 2} .0836406{col 61}{space 1}   -2.25{col 70}{space 3}0.024{col 78}{space 4} .6389923{col 91}{space 3} .9693357
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .9121425{col 50}{space 2}  .111979{col 61}{space 1}   -0.75{col 70}{space 3}0.454{col 78}{space 4} .7170231{col 91}{space 3} 1.160359
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .7963834{col 50}{space 2}  .107791{col 61}{space 1}   -1.68{col 70}{space 3}0.093{col 78}{space 4} .6107681{col 91}{space 3} 1.038408
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .8775447{col 50}{space 2} .0779128{col 61}{space 1}   -1.47{col 70}{space 3}0.141{col 78}{space 4} .7373476{col 91}{space 3} 1.044399
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  1.26827{col 50}{space 2}  .116173{col 61}{space 1}    2.59{col 70}{space 3}0.010{col 78}{space 4} 1.059786{col 91}{space 3} 1.517768
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9688247{col 50}{space 2} .0945639{col 61}{space 1}   -0.32{col 70}{space 3}0.746{col 78}{space 4} .8000861{col 91}{space 3}  1.17315
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9331544{col 50}{space 2} .1706257{col 61}{space 1}   -0.38{col 70}{space 3}0.705{col 78}{space 4} .6520264{col 91}{space 3} 1.335494
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9994992{col 50}{space 2} .1454519{col 61}{space 1}   -0.00{col 70}{space 3}0.997{col 78}{space 4} .7514043{col 91}{space 3} 1.329509
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8543309{col 50}{space 2} .2446505{col 61}{space 1}   -0.55{col 70}{space 3}0.583{col 78}{space 4} .4873009{col 91}{space 3} 1.497804
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}   1.0992{col 50}{space 2} .1850128{col 61}{space 1}    0.56{col 70}{space 3}0.574{col 78}{space 4} .7902459{col 91}{space 3} 1.528943
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.003071{col 50}{space 2} .1648735{col 61}{space 1}    0.02{col 70}{space 3}0.985{col 78}{space 4}  .726738{col 91}{space 3} 1.384476
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.023778{col 50}{space 2} .1646043{col 61}{space 1}    0.15{col 70}{space 3}0.884{col 78}{space 4} .7469762{col 91}{space 3} 1.403153
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.060449{col 50}{space 2} .1731385{col 61}{space 1}    0.36{col 70}{space 3}0.719{col 78}{space 4} .7699674{col 91}{space 3} 1.460519
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}   .94564{col 50}{space 2} .1172553{col 61}{space 1}   -0.45{col 70}{space 3}0.652{col 78}{space 4} .7415634{col 91}{space 3} 1.205878
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.000113{col 50}{space 2} .1883523{col 61}{space 1}    0.00{col 70}{space 3}1.000{col 78}{space 4} .6913415{col 91}{space 3} 1.446791
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .7392922{col 50}{space 2} .1278909{col 61}{space 1}   -1.75{col 70}{space 3}0.081{col 78}{space 4} .5266486{col 91}{space 3} 1.037794
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .8875048{col 50}{space 2} .2078441{col 61}{space 1}   -0.51{col 70}{space 3}0.610{col 78}{space 4} .5607477{col 91}{space 3} 1.404669
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.132371{col 50}{space 2} .1272744{col 61}{space 1}    1.11{col 70}{space 3}0.269{col 78}{space 4} .9084218{col 91}{space 3} 1.411529
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.293345{col 50}{space 2} .3083182{col 61}{space 1}    1.08{col 70}{space 3}0.281{col 78}{space 4} .8104681{col 91}{space 3}  2.06392
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.012834{col 50}{space 2} .2390897{col 61}{space 1}    0.05{col 70}{space 3}0.957{col 78}{space 4} .6375913{col 91}{space 3}  1.60892
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2}  1.47375{col 50}{space 2} .3200393{col 61}{space 1}    1.79{col 70}{space 3}0.074{col 78}{space 4} .9627655{col 91}{space 3} 2.255938
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .8816561{col 50}{space 2}   .12698{col 61}{space 1}   -0.87{col 70}{space 3}0.382{col 78}{space 4} .6647648{col 91}{space 3} 1.169312
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.180469{col 50}{space 2} .1459234{col 61}{space 1}    1.34{col 70}{space 3}0.180{col 78}{space 4} .9264062{col 91}{space 3} 1.504208
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .3089628{col 50}{space 2}  .086092{col 61}{space 1}   -4.22{col 70}{space 3}0.000{col 78}{space 4} .1789155{col 91}{space 3}  .533537
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 2: Improve 
. svy: logit AI_improve ib2.employmentstatus5 if infullmodel_TableD1 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   1{txt},{res}   3962{txt}){col 67}= {res}     32.56
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}  Linearized
{col 1}       AI_improve{col 19}{c |} Odds Ratio{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
employmentstatus5 {c |}
{space 7}FT Worker  {c |}{col 19}{res}{space 2} 3.037998{col 31}{space 2}  .591625{col 42}{space 1}    5.71{col 51}{space 3}0.000{col 59}{space 4} 2.073826{col 72}{space 3} 4.450437
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} .0412497{col 31}{space 2} .0075857{col 42}{space 1}  -17.34{col 51}{space 3}0.000{col 59}{space 4} .0287634{col 72}{space 3} .0591563
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_improve ib2.employmentstatus5 $controls_demographics if infullmodel_TableD1 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      9.00
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                          AI_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} 2.098102{col 50}{space 2} .4292798{col 61}{space 1}    3.62{col 70}{space 3}0.000{col 78}{space 4} 1.404799{col 91}{space 3} 3.133567
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .5859237{col 50}{space 2} .0967461{col 61}{space 1}   -3.24{col 70}{space 3}0.001{col 78}{space 4} .4238869{col 91}{space 3} .8099013
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .4313816{col 50}{space 2} .0848871{col 61}{space 1}   -4.27{col 70}{space 3}0.000{col 78}{space 4} .2932994{col 91}{space 3} .6344712
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6274358{col 50}{space 2} .0815649{col 61}{space 1}   -3.59{col 70}{space 3}0.000{col 78}{space 4} .4862747{col 91}{space 3} .8095748
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.753261{col 50}{space 2} .2386836{col 61}{space 1}    4.12{col 70}{space 3}0.000{col 78}{space 4} 1.342552{col 91}{space 3} 2.289613
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2}  .703992{col 50}{space 2} .1022251{col 61}{space 1}   -2.42{col 70}{space 3}0.016{col 78}{space 4} .5295767{col 91}{space 3} .9358507
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .6980172{col 50}{space 2} .1996649{col 61}{space 1}   -1.26{col 70}{space 3}0.209{col 78}{space 4} .3983906{col 91}{space 3} 1.222991
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.045743{col 50}{space 2} .2381017{col 61}{space 1}    0.20{col 70}{space 3}0.844{col 78}{space 4} .6692037{col 91}{space 3} 1.634148
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.253748{col 50}{space 2} .6701505{col 61}{space 1}    0.42{col 70}{space 3}0.672{col 78}{space 4} .4396317{col 91}{space 3} 3.575457
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5114603{col 50}{space 2} .1552909{col 61}{space 1}   -2.21{col 70}{space 3}0.027{col 78}{space 4} .2820259{col 91}{space 3} .9275449
{txt}{space 34}3  {c |}{col 38}{res}{space 2}  .431203{col 50}{space 2} .1200384{col 61}{space 1}   -3.02{col 70}{space 3}0.003{col 78}{space 4} .2498341{col 91}{space 3} .7442382
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .4891384{col 50}{space 2} .1216417{col 61}{space 1}   -2.88{col 70}{space 3}0.004{col 78}{space 4}   .30039{col 91}{space 3} .7964859
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .6643646{col 50}{space 2} .1598227{col 61}{space 1}   -1.70{col 70}{space 3}0.089{col 78}{space 4} .4145481{col 91}{space 3} 1.064727
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .8602597{col 50}{space 2} .1431378{col 61}{space 1}   -0.90{col 70}{space 3}0.366{col 78}{space 4} .6208056{col 91}{space 3} 1.192075
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}  .483637{col 50}{space 2} .1091257{col 61}{space 1}   -3.22{col 70}{space 3}0.001{col 78}{space 4} .3107417{col 91}{space 3} .7527305
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .4243676{col 50}{space 2} .1168096{col 61}{space 1}   -3.11{col 70}{space 3}0.002{col 78}{space 4} .2473845{col 91}{space 3} .7279674
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .3840638{col 50}{space 2} .1048289{col 61}{space 1}   -3.51{col 70}{space 3}0.000{col 78}{space 4} .2249053{col 91}{space 3} .6558537
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2249556{col 50}{space 2} .0741438{col 61}{space 1}   -4.53{col 70}{space 3}0.000{col 78}{space 4} .1178857{col 91}{space 3} .4292719
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_improve ib2.employmentstatus5 $controls_demographics $controls_objective if infullmodel_TableD1 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      7.85
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                          AI_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} 2.065017{col 50}{space 2} .4231148{col 61}{space 1}    3.54{col 70}{space 3}0.000{col 78}{space 4} 1.381854{col 91}{space 3} 3.085924
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .5967726{col 50}{space 2} .0987241{col 61}{space 1}   -3.12{col 70}{space 3}0.002{col 78}{space 4}  .431471{col 91}{space 3} .8254034
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .4389943{col 50}{space 2} .0868928{col 61}{space 1}   -4.16{col 70}{space 3}0.000{col 78}{space 4} .2977995{col 91}{space 3} .6471334
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6317683{col 50}{space 2} .0833817{col 61}{space 1}   -3.48{col 70}{space 3}0.001{col 78}{space 4} .4877314{col 91}{space 3} .8183422
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.732196{col 50}{space 2} .2368375{col 61}{space 1}    4.02{col 70}{space 3}0.000{col 78}{space 4} 1.324889{col 91}{space 3} 2.264721
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .6974604{col 50}{space 2} .1028677{col 61}{space 1}   -2.44{col 70}{space 3}0.015{col 78}{space 4} .5223221{col 91}{space 3} .9313239
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .6836831{col 50}{space 2} .1929683{col 61}{space 1}   -1.35{col 70}{space 3}0.178{col 78}{space 4} .3931256{col 91}{space 3}  1.18899
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.129698{col 50}{space 2} .2598135{col 61}{space 1}    0.53{col 70}{space 3}0.596{col 78}{space 4} .7196789{col 91}{space 3} 1.773314
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.339877{col 50}{space 2} .7204718{col 61}{space 1}    0.54{col 70}{space 3}0.586{col 78}{space 4} .4668973{col 91}{space 3} 3.845108
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5083028{col 50}{space 2} .1523285{col 61}{space 1}   -2.26{col 70}{space 3}0.024{col 78}{space 4} .2824594{col 91}{space 3}  .914722
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .4113217{col 50}{space 2} .1135379{col 61}{space 1}   -3.22{col 70}{space 3}0.001{col 78}{space 4} .2394149{col 91}{space 3} .7066626
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .4648291{col 50}{space 2} .1149079{col 61}{space 1}   -3.10{col 70}{space 3}0.002{col 78}{space 4} .2862912{col 91}{space 3} .7547074
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .6062726{col 50}{space 2} .1449004{col 61}{space 1}   -2.09{col 70}{space 3}0.036{col 78}{space 4} .3794607{col 91}{space 3} .9686549
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.010877{col 50}{space 2} .1838329{col 61}{space 1}    0.06{col 70}{space 3}0.953{col 78}{space 4} .7077112{col 91}{space 3} 1.443913
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.090424{col 50}{space 2} .3315547{col 61}{space 1}    0.28{col 70}{space 3}0.776{col 78}{space 4} .6007582{col 91}{space 3} 1.979207
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .7050556{col 50}{space 2} .2294216{col 61}{space 1}   -1.07{col 70}{space 3}0.283{col 78}{space 4} .3725306{col 91}{space 3} 1.334396
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}  .859264{col 50}{space 2} .2912932{col 61}{space 1}   -0.45{col 70}{space 3}0.655{col 78}{space 4} .4420572{col 91}{space 3} 1.670224
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.600942{col 50}{space 2} .2810704{col 61}{space 1}    2.68{col 70}{space 3}0.007{col 78}{space 4} 1.134718{col 91}{space 3} 2.258723
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.005969{col 50}{space 2} .3909627{col 61}{space 1}    0.02{col 70}{space 3}0.988{col 78}{space 4} .4695373{col 91}{space 3} 2.155257
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2}  1.09556{col 50}{space 2} .4040863{col 61}{space 1}    0.25{col 70}{space 3}0.805{col 78}{space 4} .5315977{col 91}{space 3}  2.25782
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .3170697{col 50}{space 2}  .116649{col 61}{space 1}   -3.12{col 70}{space 3}0.002{col 78}{space 4} .1541361{col 91}{space 3} .6522364
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.003973{col 50}{space 2} .2001391{col 61}{space 1}    0.02{col 70}{space 3}0.984{col 78}{space 4} .6791814{col 91}{space 3} 1.484084
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2}   .88363{col 50}{space 2} .1492986{col 61}{space 1}   -0.73{col 70}{space 3}0.464{col 78}{space 4}  .634464{col 91}{space 3} 1.230648
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .1480962{col 50}{space 2} .0590534{col 61}{space 1}   -4.79{col 70}{space 3}0.000{col 78}{space 4} .0677678{col 91}{space 3} .3236416
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 3: Worsen v Improve
. svy: logit AI_directionworse ib2.employmentstatus5 if infullmodel_TableD1 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}   1{txt},{res}   1321{txt}){col 67}= {res}     27.86
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}  Linearized
{col 1}AI_directionworse{col 19}{c |} Odds Ratio{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
employmentstatus5 {c |}
{space 7}FT Worker  {c |}{col 19}{res}{space 2} .3361887{col 31}{space 2} .0694322{col 42}{space 1}   -5.28{col 51}{space 3}0.000{col 59}{space 4} .2241946{col 72}{space 3} .5041281
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 6.226455{col 31}{space 2} 1.203046{col 42}{space 1}    9.47{col 51}{space 3}0.000{col 59}{space 4} 4.262113{col 72}{space 3} 9.096132
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_directionworse ib2.employmentstatus5 $controls_demographics if infullmodel_TableD1 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}  17{txt},{res}   1305{txt}){col 67}= {res}      4.64
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                   AI_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} .4188268{col 50}{space 2} .0960455{col 61}{space 1}   -3.80{col 70}{space 3}0.000{col 78}{space 4} .2670899{col 91}{space 3} .6567671
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.630793{col 50}{space 2} .3050056{col 61}{space 1}    2.61{col 70}{space 3}0.009{col 78}{space 4} 1.129935{col 91}{space 3} 2.353663
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.731111{col 50}{space 2} .3690646{col 61}{space 1}    2.57{col 70}{space 3}0.010{col 78}{space 4} 1.139427{col 91}{space 3} 2.630047
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} 1.343212{col 50}{space 2} .1961699{col 61}{space 1}    2.02{col 70}{space 3}0.044{col 78}{space 4} 1.008595{col 91}{space 3} 1.788845
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7325523{col 50}{space 2} .1120903{col 61}{space 1}   -2.03{col 70}{space 3}0.042{col 78}{space 4} .5425927{col 91}{space 3}  .989016
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.240947{col 50}{space 2} .2031517{col 61}{space 1}    1.32{col 70}{space 3}0.188{col 78}{space 4} .9000731{col 91}{space 3} 1.710916
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}  1.22502{col 50}{space 2} .4068413{col 61}{space 1}    0.61{col 70}{space 3}0.541{col 78}{space 4} .6385446{col 91}{space 3} 2.350146
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.053222{col 50}{space 2} .2908356{col 61}{space 1}    0.19{col 70}{space 3}0.851{col 78}{space 4} .6127091{col 91}{space 3} 1.810445
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .6364657{col 50}{space 2}  .396653{col 61}{space 1}   -0.72{col 70}{space 3}0.469{col 78}{space 4} .1874172{col 91}{space 3} 2.161426
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.921637{col 50}{space 2} .6822025{col 61}{space 1}    1.84{col 70}{space 3}0.066{col 78}{space 4} .9576527{col 91}{space 3} 3.855977
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 2.194025{col 50}{space 2} .7060782{col 61}{space 1}    2.44{col 70}{space 3}0.015{col 78}{space 4} 1.166965{col 91}{space 3} 4.125013
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.954344{col 50}{space 2} .5733307{col 61}{space 1}    2.28{col 70}{space 3}0.023{col 78}{space 4} 1.099161{col 91}{space 3} 3.474884
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.511639{col 50}{space 2} .4308267{col 61}{space 1}    1.45{col 70}{space 3}0.147{col 78}{space 4} .8642265{col 91}{space 3} 2.644042
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.146499{col 50}{space 2} .2142211{col 61}{space 1}    0.73{col 70}{space 3}0.464{col 78}{space 4} .7946625{col 91}{space 3} 1.654112
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 2.509639{col 50}{space 2} .6072188{col 61}{space 1}    3.80{col 70}{space 3}0.000{col 78}{space 4} 1.561242{col 91}{space 3} 4.034152
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.649087{col 50}{space 2} .5168764{col 61}{space 1}    1.60{col 70}{space 3}0.111{col 78}{space 4}  .891672{col 91}{space 3} 3.049874
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.619572{col 50}{space 2} .4988888{col 61}{space 1}    1.57{col 70}{space 3}0.118{col 78}{space 4} .8850298{col 91}{space 3} 2.963757
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 1.597944{col 50}{space 2} .5904314{col 61}{space 1}    1.27{col 70}{space 3}0.205{col 78}{space 4} .7740322{col 91}{space 3} 3.298863
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit AI_directionworse ib2.employmentstatus5 $controls_demographics $controls_objective if infullmodel_TableD1 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,322
{txt}{col 1}Number of PSUs{col 20}= {res}    1,322{txt}{col 49}Population size{col 67}={res}  1,349.414
{txt}{col 49}Design df{col 67}= {res}     1,321
{txt}{col 49}F({res}  23{txt},{res}   1299{txt}){col 67}= {res}      4.25
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                   AI_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2}   .41444{col 50}{space 2} .0947479{col 61}{space 1}   -3.85{col 70}{space 3}0.000{col 78}{space 4} .2646575{col 91}{space 3} .6489916
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.643828{col 50}{space 2} .3076717{col 61}{space 1}    2.66{col 70}{space 3}0.008{col 78}{space 4} 1.138657{col 91}{space 3} 2.373122
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.718449{col 50}{space 2} .3686662{col 61}{space 1}    2.52{col 70}{space 3}0.012{col 78}{space 4} 1.128125{col 91}{space 3} 2.617677
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} 1.335884{col 50}{space 2} .1975336{col 61}{space 1}    1.96{col 70}{space 3}0.050{col 78}{space 4} .9995127{col 91}{space 3} 1.785456
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7356201{col 50}{space 2} .1132835{col 61}{space 1}   -1.99{col 70}{space 3}0.046{col 78}{space 4} .5438144{col 91}{space 3} .9950766
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.315665{col 50}{space 2} .2214155{col 61}{space 1}    1.63{col 70}{space 3}0.103{col 78}{space 4} .9457228{col 91}{space 3} 1.830319
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.318259{col 50}{space 2} .4386386{col 61}{space 1}    0.83{col 70}{space 3}0.406{col 78}{space 4}  .686296{col 91}{space 3} 2.532153
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2}  .926384{col 50}{space 2} .2584575{col 61}{space 1}   -0.27{col 70}{space 3}0.784{col 78}{space 4} .5359093{col 91}{space 3} 1.601367
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5966104{col 50}{space 2} .3877683{col 61}{space 1}   -0.79{col 70}{space 3}0.427{col 78}{space 4} .1667029{col 91}{space 3}   2.1352
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.823815{col 50}{space 2} .6373137{col 61}{space 1}    1.72{col 70}{space 3}0.086{col 78}{space 4} .9188914{col 91}{space 3} 3.619906
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 2.220856{col 50}{space 2} .7064391{col 61}{space 1}    2.51{col 70}{space 3}0.012{col 78}{space 4} 1.189901{col 91}{space 3} 4.145053
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.988575{col 50}{space 2} .5775212{col 61}{space 1}    2.37{col 70}{space 3}0.018{col 78}{space 4} 1.124889{col 91}{space 3} 3.515397
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.569265{col 50}{space 2} .4418033{col 61}{space 1}    1.60{col 70}{space 3}0.110{col 78}{space 4} .9033028{col 91}{space 3}  2.72621
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}  .926379{col 50}{space 2} .1941269{col 61}{space 1}   -0.36{col 70}{space 3}0.715{col 78}{space 4} .6141183{col 91}{space 3} 1.397415
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} .8738151{col 50}{space 2} .3061096{col 61}{space 1}   -0.39{col 70}{space 3}0.700{col 78}{space 4} .4394994{col 91}{space 3} 1.737324
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.107186{col 50}{space 2} .4118154{col 61}{space 1}    0.27{col 70}{space 3}0.784{col 78}{space 4} .5337372{col 91}{space 3} 2.296748
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .9733152{col 50}{space 2}   .39149{col 61}{space 1}   -0.07{col 70}{space 3}0.946{col 78}{space 4} .4421467{col 91}{space 3} 2.142597
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .7403207{col 50}{space 2} .1530683{col 61}{space 1}   -1.45{col 70}{space 3}0.146{col 78}{space 4} .4934744{col 91}{space 3} 1.110645
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.080125{col 50}{space 2} .4675268{col 61}{space 1}    0.18{col 70}{space 3}0.859{col 78}{space 4} .4620594{col 91}{space 3} 2.524936
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} .8527179{col 50}{space 2} .3655586{col 61}{space 1}   -0.37{col 70}{space 3}0.710{col 78}{space 4} .3677581{col 91}{space 3} 1.977191
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} 3.897365{col 50}{space 2}  1.55288{col 61}{space 1}    3.41{col 70}{space 3}0.001{col 78}{space 4} 1.783628{col 91}{space 3} 8.516043
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .9586039{col 50}{space 2} .2224665{col 61}{space 1}   -0.18{col 70}{space 3}0.855{col 78}{space 4} .6080186{col 91}{space 3} 1.511338
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.227994{col 50}{space 2} .2313644{col 61}{space 1}    1.09{col 70}{space 3}0.276{col 78}{space 4} .8485466{col 91}{space 3}  1.77712
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 2.090557{col 50}{space 2} .9456777{col 61}{space 1}    1.63{col 70}{space 3}0.103{col 78}{space 4} .8607202{col 91}{space 3}  5.07764
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. 
. 
. ************* TABLE D2: Demographic Predictors of Subjective Exposure to General Tech. Advances ***************
. 
. svy: logit tech_worsen aiexposure_felten2021  $controls_demographics $controls_objective, or
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}note: aiexposure_felten2021 omitted because of collinearity

Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      2.57
{txt}{col 49}Prob > F{col 67}= {res}    0.0001

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                         tech_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .9881793{col 50}{space 2} .1211732{col 61}{space 1}   -0.10{col 70}{space 3}0.923{col 78}{space 4} .7770111{col 91}{space 3} 1.256737
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.316433{col 50}{space 2} .1963717{col 61}{space 1}    1.84{col 70}{space 3}0.065{col 78}{space 4}  .982622{col 91}{space 3} 1.763644
{txt}{space 32}50+  {c |}{col 38}{res}{space 2}  1.29173{col 50}{space 2} .2050114{col 61}{space 1}    1.61{col 70}{space 3}0.107{col 78}{space 4} .9463145{col 91}{space 3} 1.763225
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}  .868358{col 50}{space 2} .0862481{col 61}{space 1}   -1.42{col 70}{space 3}0.155{col 78}{space 4} .7147084{col 91}{space 3} 1.055039
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  1.10359{col 50}{space 2}  .116495{col 61}{space 1}    0.93{col 70}{space 3}0.350{col 78}{space 4} .8972786{col 91}{space 3} 1.357338
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.248167{col 50}{space 2} .1471494{col 61}{space 1}    1.88{col 70}{space 3}0.060{col 78}{space 4} .9905848{col 91}{space 3} 1.572728
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.037156{col 50}{space 2} .1137139{col 61}{space 1}    0.33{col 70}{space 3}0.739{col 78}{space 4} .8365462{col 91}{space 3} 1.285874
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9606719{col 50}{space 2} .2051961{col 61}{space 1}   -0.19{col 70}{space 3}0.851{col 78}{space 4} .6319838{col 91}{space 3} 1.460307
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .8244381{col 50}{space 2} .1417613{col 61}{space 1}   -1.12{col 70}{space 3}0.262{col 78}{space 4} .5885049{col 91}{space 3} 1.154958
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2}  .951978{col 50}{space 2} .2910022{col 61}{space 1}   -0.16{col 70}{space 3}0.872{col 78}{space 4} .5228182{col 91}{space 3} 1.733417
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.161317{col 50}{space 2} .2124477{col 61}{space 1}    0.82{col 70}{space 3}0.414{col 78}{space 4} .8113102{col 91}{space 3} 1.662319
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.074199{col 50}{space 2} .1910727{col 61}{space 1}    0.40{col 70}{space 3}0.687{col 78}{space 4} .7579338{col 91}{space 3} 1.522433
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.133929{col 50}{space 2} .2019321{col 61}{space 1}    0.71{col 70}{space 3}0.480{col 78}{space 4} .7997533{col 91}{space 3} 1.607739
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.255271{col 50}{space 2} .2217886{col 61}{space 1}    1.29{col 70}{space 3}0.198{col 78}{space 4} .8877618{col 91}{space 3}  1.77492
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.145882{col 50}{space 2} .1645278{col 61}{space 1}    0.95{col 70}{space 3}0.343{col 78}{space 4} .8647401{col 91}{space 3} 1.518428
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.295141{col 50}{space 2} .2747104{col 61}{space 1}    1.22{col 70}{space 3}0.223{col 78}{space 4}  .854505{col 91}{space 3} 1.962995
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .9488207{col 50}{space 2} .1787633{col 61}{space 1}   -0.28{col 70}{space 3}0.780{col 78}{space 4} .6557886{col 91}{space 3} 1.372791
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.531672{col 50}{space 2} .3715764{col 61}{space 1}    1.76{col 70}{space 3}0.079{col 78}{space 4} .9519308{col 91}{space 3} 2.464486
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2}        1{col 50}{txt}  (omitted)
{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.529412{col 50}{space 2} .4135439{col 61}{space 1}    1.57{col 70}{space 3}0.116{col 78}{space 4} .9001072{col 91}{space 3} 2.598691
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.281864{col 50}{space 2} .3432878{col 61}{space 1}    0.93{col 70}{space 3}0.354{col 78}{space 4} .7582589{col 91}{space 3} 2.167038
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} 1.394314{col 50}{space 2} .3512092{col 61}{space 1}    1.32{col 70}{space 3}0.187{col 78}{space 4} .8509197{col 91}{space 3} 2.284717
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .8428629{col 50}{space 2} .1464735{col 61}{space 1}   -0.98{col 70}{space 3}0.325{col 78}{space 4} .5994999{col 91}{space 3} 1.185017
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.179514{col 50}{space 2} .1735202{col 61}{space 1}    1.12{col 70}{space 3}0.262{col 78}{space 4} .8839814{col 91}{space 3} 1.573849
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .0859055{col 50}{space 2} .0244577{col 61}{space 1}   -8.62{col 70}{space 3}0.000{col 78}{space 4} .0491593{col 91}{space 3}  .150119
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. generate infullmodel_TableD2 = e(sample) 
{txt}
{com}.          
.          
. ******* Age Group ********
. 
. * Model 1: Worsen 
. svy: logit tech_worsen ib3.age_3 if infullmodel_TableD2 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   2{txt},{res}   3961{txt}){col 67}= {res}      1.59
{txt}{col 49}Prob > F{col 67}= {res}    0.2031

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1} tech_worsen{col 14}{c |} Odds Ratio{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}age_3group {c |}
{space 6}18-29  {c |}{col 14}{res}{space 2}  .783657{col 26}{space 2} .1210651{col 37}{space 1}   -1.58{col 46}{space 3}0.115{col 54}{space 4} .5788765{col 67}{space 3} 1.060879
{txt}{space 6}30-49  {c |}{col 14}{res}{space 2} 1.019294{col 26}{space 2}  .100864{col 37}{space 1}    0.19{col 46}{space 3}0.847{col 54}{space 4} .8395435{col 67}{space 3}  1.23753
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} .2106453{col 26}{space 2} .0158455{col 37}{space 1}  -20.71{col 46}{space 3}0.000{col 54}{space 4} .1817614{col 67}{space 3} .2441192
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit tech_worsen ib3.age_3 $controls_demographics if infullmodel_TableD2 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      2.51
{txt}{col 49}Prob > F{col 67}= {res}    0.0006

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                         tech_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}age_3group {c |}
{space 30}18-29  {c |}{col 38}{res}{space 2} .7919578{col 50}{space 2} .1262588{col 61}{space 1}   -1.46{col 70}{space 3}0.144{col 78}{space 4}  .579371{col 91}{space 3} 1.082548
{txt}{space 30}30-49  {c |}{col 38}{res}{space 2} 1.020306{col 50}{space 2}  .106136{col 61}{space 1}    0.19{col 70}{space 3}0.847{col 78}{space 4} .8320669{col 91}{space 3} 1.251131
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .8669435{col 50}{space 2} .0848512{col 61}{space 1}   -1.46{col 70}{space 3}0.145{col 78}{space 4} .7155746{col 91}{space 3} 1.050332
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.110653{col 50}{space 2} .1160975{col 61}{space 1}    1.00{col 70}{space 3}0.315{col 78}{space 4} .9048459{col 91}{space 3} 1.363272
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.259801{col 50}{space 2} .1461974{col 61}{space 1}    1.99{col 70}{space 3}0.047{col 78}{space 4}  1.00344{col 91}{space 3} 1.581657
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9847405{col 50}{space 2} .1058998{col 61}{space 1}   -0.14{col 70}{space 3}0.886{col 78}{space 4} .7975444{col 91}{space 3} 1.215874
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9241554{col 50}{space 2} .1945524{col 61}{space 1}   -0.37{col 70}{space 3}0.708{col 78}{space 4} .6116401{col 91}{space 3} 1.396349
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .8155661{col 50}{space 2} .1400316{col 61}{space 1}   -1.19{col 70}{space 3}0.235{col 78}{space 4} .5824577{col 91}{space 3} 1.141968
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .9556425{col 50}{space 2} .2910917{col 61}{space 1}   -0.15{col 70}{space 3}0.882{col 78}{space 4} .5259417{col 91}{space 3} 1.736414
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.167665{col 50}{space 2} .2125261{col 61}{space 1}    0.85{col 70}{space 3}0.394{col 78}{space 4} .8172294{col 91}{space 3}  1.66837
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.069154{col 50}{space 2}  .189471{col 61}{space 1}    0.38{col 70}{space 3}0.706{col 78}{space 4} .7553496{col 91}{space 3} 1.513327
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.117951{col 50}{space 2} .1984927{col 61}{space 1}    0.63{col 70}{space 3}0.530{col 78}{space 4} .7893058{col 91}{space 3} 1.583435
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.264681{col 50}{space 2}  .224145{col 61}{space 1}    1.32{col 70}{space 3}0.185{col 78}{space 4} .8934554{col 91}{space 3}  1.79015
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.282489{col 50}{space 2} .1698614{col 61}{space 1}    1.88{col 70}{space 3}0.060{col 78}{space 4} .9891918{col 91}{space 3}  1.66275
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.814161{col 50}{space 2} .2435089{col 61}{space 1}    4.44{col 70}{space 3}0.000{col 78}{space 4} 1.394398{col 91}{space 3} 2.360287
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .8573966{col 50}{space 2} .1444471{col 61}{space 1}   -0.91{col 70}{space 3}0.361{col 78}{space 4} .6162182{col 91}{space 3} 1.192968
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.127179{col 50}{space 2} .2059426{col 61}{space 1}    0.66{col 70}{space 3}0.512{col 78}{space 4} .7878174{col 91}{space 3} 1.612725
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .1576602{col 50}{space 2}  .031355{col 61}{space 1}   -9.29{col 70}{space 3}0.000{col 78}{space 4} .1067544{col 91}{space 3} .2328405
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit tech_worsen ib3.age_3 $controls_demographics  $controls_objective if infullmodel_TableD2 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      2.57
{txt}{col 49}Prob > F{col 67}= {res}    0.0001

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                         tech_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}age_3group {c |}
{space 30}18-29  {c |}{col 38}{res}{space 2} .7741558{col 50}{space 2} .1228669{col 61}{space 1}   -1.61{col 70}{space 3}0.107{col 78}{space 4} .5671425{col 91}{space 3} 1.056731
{txt}{space 30}30-49  {c |}{col 38}{res}{space 2} 1.019124{col 50}{space 2} .1065909{col 61}{space 1}    0.18{col 70}{space 3}0.856{col 78}{space 4} .8301796{col 91}{space 3} 1.251071
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}  .868358{col 50}{space 2} .0862481{col 61}{space 1}   -1.42{col 70}{space 3}0.155{col 78}{space 4} .7147084{col 91}{space 3} 1.055039
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  1.10359{col 50}{space 2}  .116495{col 61}{space 1}    0.93{col 70}{space 3}0.350{col 78}{space 4} .8972786{col 91}{space 3} 1.357338
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.248167{col 50}{space 2} .1471494{col 61}{space 1}    1.88{col 70}{space 3}0.060{col 78}{space 4} .9905848{col 91}{space 3} 1.572728
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.037156{col 50}{space 2} .1137139{col 61}{space 1}    0.33{col 70}{space 3}0.739{col 78}{space 4} .8365462{col 91}{space 3} 1.285874
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9606719{col 50}{space 2} .2051961{col 61}{space 1}   -0.19{col 70}{space 3}0.851{col 78}{space 4} .6319838{col 91}{space 3} 1.460307
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .8244381{col 50}{space 2} .1417613{col 61}{space 1}   -1.12{col 70}{space 3}0.262{col 78}{space 4} .5885049{col 91}{space 3} 1.154958
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2}  .951978{col 50}{space 2} .2910022{col 61}{space 1}   -0.16{col 70}{space 3}0.872{col 78}{space 4} .5228182{col 91}{space 3} 1.733417
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.161317{col 50}{space 2} .2124477{col 61}{space 1}    0.82{col 70}{space 3}0.414{col 78}{space 4} .8113102{col 91}{space 3} 1.662319
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.074199{col 50}{space 2} .1910727{col 61}{space 1}    0.40{col 70}{space 3}0.687{col 78}{space 4} .7579338{col 91}{space 3} 1.522433
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.133929{col 50}{space 2} .2019321{col 61}{space 1}    0.71{col 70}{space 3}0.480{col 78}{space 4} .7997533{col 91}{space 3} 1.607739
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.255271{col 50}{space 2} .2217886{col 61}{space 1}    1.29{col 70}{space 3}0.198{col 78}{space 4} .8877618{col 91}{space 3}  1.77492
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.145882{col 50}{space 2} .1645278{col 61}{space 1}    0.95{col 70}{space 3}0.343{col 78}{space 4} .8647401{col 91}{space 3} 1.518428
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.295141{col 50}{space 2} .2747104{col 61}{space 1}    1.22{col 70}{space 3}0.223{col 78}{space 4}  .854505{col 91}{space 3} 1.962995
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .9488207{col 50}{space 2} .1787633{col 61}{space 1}   -0.28{col 70}{space 3}0.780{col 78}{space 4} .6557886{col 91}{space 3} 1.372791
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.531672{col 50}{space 2} .3715764{col 61}{space 1}    1.76{col 70}{space 3}0.079{col 78}{space 4} .9519308{col 91}{space 3} 2.464486
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .9881793{col 50}{space 2} .1211732{col 61}{space 1}   -0.10{col 70}{space 3}0.923{col 78}{space 4} .7770111{col 91}{space 3} 1.256737
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.529412{col 50}{space 2} .4135439{col 61}{space 1}    1.57{col 70}{space 3}0.116{col 78}{space 4} .9001072{col 91}{space 3} 2.598691
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.281864{col 50}{space 2} .3432878{col 61}{space 1}    0.93{col 70}{space 3}0.354{col 78}{space 4} .7582589{col 91}{space 3} 2.167038
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} 1.394314{col 50}{space 2} .3512092{col 61}{space 1}    1.32{col 70}{space 3}0.187{col 78}{space 4} .8509197{col 91}{space 3} 2.284717
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .8428629{col 50}{space 2} .1464735{col 61}{space 1}   -0.98{col 70}{space 3}0.325{col 78}{space 4} .5994999{col 91}{space 3} 1.185017
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.179514{col 50}{space 2} .1735202{col 61}{space 1}    1.12{col 70}{space 3}0.262{col 78}{space 4} .8839814{col 91}{space 3} 1.573849
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .1109666{col 50}{space 2} .0309115{col 61}{space 1}   -7.89{col 70}{space 3}0.000{col 78}{space 4} .0642694{col 91}{space 3} .1915935
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 2: Improve 
. svy: logit tech_improve ib3.age_3 if infullmodel_TableD2 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   2{txt},{res}   3961{txt}){col 67}= {res}     23.20
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}tech_improve{col 14}{c |} Odds Ratio{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}age_3group {c |}
{space 6}18-29  {c |}{col 14}{res}{space 2}  2.56275{col 26}{space 2} .3637471{col 37}{space 1}    6.63{col 46}{space 3}0.000{col 54}{space 4} 1.940229{col 67}{space 3} 3.385007
{txt}{space 6}30-49  {c |}{col 14}{res}{space 2} 1.702237{col 26}{space 2} .1887627{col 37}{space 1}    4.80{col 46}{space 3}0.000{col 54}{space 4} 1.369622{col 67}{space 3} 2.115629
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}  .123635{col 26}{space 2}   .01121{col 37}{space 1}  -23.06{col 46}{space 3}0.000{col 54}{space 4} .1034997{col 67}{space 3} .1476875
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit tech_improve ib3.age_3 $controls_demographics if infullmodel_TableD2 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}     10.37
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                        tech_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}age_3group {c |}
{space 30}18-29  {c |}{col 38}{res}{space 2} 2.372031{col 50}{space 2} .3588677{col 61}{space 1}    5.71{col 70}{space 3}0.000{col 78}{space 4}   1.7632{col 91}{space 3}  3.19109
{txt}{space 30}30-49  {c |}{col 38}{res}{space 2} 1.419674{col 50}{space 2} .1686644{col 61}{space 1}    2.95{col 70}{space 3}0.003{col 78}{space 4} 1.124684{col 91}{space 3} 1.792035
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6601585{col 50}{space 2} .0671241{col 61}{space 1}   -4.08{col 70}{space 3}0.000{col 78}{space 4} .5408448{col 91}{space 3} .8057937
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.265467{col 50}{space 2}  .133295{col 61}{space 1}    2.24{col 70}{space 3}0.025{col 78}{space 4} 1.029353{col 91}{space 3} 1.555742
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2}  .743296{col 50}{space 2} .1110058{col 61}{space 1}   -1.99{col 70}{space 3}0.047{col 78}{space 4} .5546286{col 91}{space 3} .9961423
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .7884021{col 50}{space 2} .0885944{col 61}{space 1}   -2.12{col 70}{space 3}0.034{col 78}{space 4} .6325097{col 91}{space 3} .9827167
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .7632024{col 50}{space 2} .1782472{col 61}{space 1}   -1.16{col 70}{space 3}0.247{col 78}{space 4} .4828136{col 91}{space 3} 1.206424
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9818739{col 50}{space 2}  .171683{col 61}{space 1}   -0.10{col 70}{space 3}0.917{col 78}{space 4} .6969083{col 91}{space 3} 1.383362
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8789707{col 50}{space 2} .3416891{col 61}{space 1}   -0.33{col 70}{space 3}0.740{col 78}{space 4} .4101844{col 91}{space 3} 1.883517
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5046275{col 50}{space 2} .1133756{col 61}{space 1}   -3.04{col 70}{space 3}0.002{col 78}{space 4} .3248415{col 91}{space 3} .7839176
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .5393284{col 50}{space 2} .1130688{col 61}{space 1}   -2.95{col 70}{space 3}0.003{col 78}{space 4}  .357558{col 91}{space 3} .8135046
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .6527976{col 50}{space 2} .1279229{col 61}{space 1}   -2.18{col 70}{space 3}0.030{col 78}{space 4} .4445547{col 91}{space 3} .9585877
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .8106742{col 50}{space 2} .1603535{col 61}{space 1}   -1.06{col 70}{space 3}0.289{col 78}{space 4} .5500789{col 91}{space 3} 1.194724
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}  .783422{col 50}{space 2} .1021823{col 61}{space 1}   -1.87{col 70}{space 3}0.061{col 78}{space 4} .6066511{col 91}{space 3} 1.011702
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} .4522395{col 50}{space 2}  .074826{col 61}{space 1}   -4.80{col 70}{space 3}0.000{col 78}{space 4} .3269555{col 91}{space 3} .6255304
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .4080237{col 50}{space 2} .0803554{col 61}{space 1}   -4.55{col 70}{space 3}0.000{col 78}{space 4} .2773321{col 91}{space 3} .6003032
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .4044302{col 50}{space 2}  .082221{col 61}{space 1}   -4.45{col 70}{space 3}0.000{col 78}{space 4} .2714817{col 91}{space 3} .6024853
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .3595777{col 50}{space 2} .0784227{col 61}{space 1}   -4.69{col 70}{space 3}0.000{col 78}{space 4} .2344724{col 91}{space 3} .5514342
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit tech_improve ib3.age_3 $controls_demographics $controls_objective if infullmodel_TableD2 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      8.76
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                        tech_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}age_3group {c |}
{space 30}18-29  {c |}{col 38}{res}{space 2} 2.326713{col 50}{space 2} .3518716{col 61}{space 1}    5.58{col 70}{space 3}0.000{col 78}{space 4} 1.729718{col 91}{space 3} 3.129756
{txt}{space 30}30-49  {c |}{col 38}{res}{space 2} 1.409894{col 50}{space 2} .1678866{col 61}{space 1}    2.88{col 70}{space 3}0.004{col 78}{space 4}  1.11634{col 91}{space 3} 1.780641
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6844365{col 50}{space 2} .0702137{col 61}{space 1}   -3.70{col 70}{space 3}0.000{col 78}{space 4} .5597383{col 91}{space 3} .8369149
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.259845{col 50}{space 2}  .132008{col 61}{space 1}    2.20{col 70}{space 3}0.028{col 78}{space 4} 1.025888{col 91}{space 3} 1.547157
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .7404869{col 50}{space 2} .1100479{col 61}{space 1}   -2.02{col 70}{space 3}0.043{col 78}{space 4} .5533208{col 91}{space 3} .9909639
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .8021883{col 50}{space 2} .0926664{col 61}{space 1}   -1.91{col 70}{space 3}0.056{col 78}{space 4} .6396141{col 91}{space 3} 1.006085
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .7757171{col 50}{space 2} .1779182{col 61}{space 1}   -1.11{col 70}{space 3}0.268{col 78}{space 4} .4947803{col 91}{space 3}  1.21617
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.063545{col 50}{space 2} .1872545{col 61}{space 1}    0.35{col 70}{space 3}0.726{col 78}{space 4} .7530814{col 91}{space 3} 1.501998
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .9270945{col 50}{space 2} .3635164{col 61}{space 1}   -0.19{col 70}{space 3}0.847{col 78}{space 4} .4297971{col 91}{space 3} 1.999791
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5084147{col 50}{space 2} .1134948{col 61}{space 1}   -3.03{col 70}{space 3}0.002{col 78}{space 4} .3282041{col 91}{space 3} .7875756
{txt}{space 34}3  {c |}{col 38}{res}{space 2}  .526227{col 50}{space 2} .1103904{col 61}{space 1}   -3.06{col 70}{space 3}0.002{col 78}{space 4} .3487835{col 91}{space 3} .7939448
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .6499105{col 50}{space 2} .1271704{col 61}{space 1}   -2.20{col 70}{space 3}0.028{col 78}{space 4} .4428379{col 91}{space 3} .9538108
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .7725892{col 50}{space 2} .1535353{col 61}{space 1}   -1.30{col 70}{space 3}0.194{col 78}{space 4}  .523286{col 91}{space 3} 1.140665
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .8730948{col 50}{space 2} .1217765{col 61}{space 1}   -0.97{col 70}{space 3}0.331{col 78}{space 4} .6642051{col 91}{space 3} 1.147679
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} .6410654{col 50}{space 2} .1550765{col 61}{space 1}   -1.84{col 70}{space 3}0.066{col 78}{space 4} .3989607{col 91}{space 3} 1.030089
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .5559549{col 50}{space 2} .1282872{col 61}{space 1}   -2.54{col 70}{space 3}0.011{col 78}{space 4}  .353642{col 91}{space 3} .8740078
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .6599147{col 50}{space 2} .1820168{col 61}{space 1}   -1.51{col 70}{space 3}0.132{col 78}{space 4} .3842721{col 91}{space 3} 1.133279
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.274265{col 50}{space 2} .1638121{col 61}{space 1}    1.89{col 70}{space 3}0.059{col 78}{space 4} .9903772{col 91}{space 3} 1.639527
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.626424{col 50}{space 2}  .455619{col 61}{space 1}    1.74{col 70}{space 3}0.083{col 78}{space 4} .9390941{col 91}{space 3} 2.816815
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.166789{col 50}{space 2}  .320711{col 61}{space 1}    0.56{col 70}{space 3}0.575{col 78}{space 4} .6806977{col 91}{space 3} 2.000002
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .4380315{col 50}{space 2} .1188453{col 61}{space 1}   -3.04{col 70}{space 3}0.002{col 78}{space 4} .2573294{col 91}{space 3} .7456262
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .8297709{col 50}{space 2} .1214935{col 61}{space 1}   -1.27{col 70}{space 3}0.203{col 78}{space 4} .6227142{col 91}{space 3} 1.105675
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} .6009114{col 50}{space 2} .0793849{col 61}{space 1}   -3.86{col 70}{space 3}0.000{col 78}{space 4} .4637949{col 91}{space 3}  .778565
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2647897{col 50}{space 2} .0778736{col 61}{space 1}   -4.52{col 70}{space 3}0.000{col 78}{space 4} .1487612{col 91}{space 3} .4713165
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 3: Worsen v Improve
. svy: logit tech_directionworse ib3.age_3 if infullmodel_TableD2 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,324
{txt}{col 1}Number of PSUs{col 20}= {res}    1,324{txt}{col 49}Population size{col 67}={res} 1,363.3793
{txt}{col 49}Design df{col 67}= {res}     1,323
{txt}{col 49}F({res}   2{txt},{res}   1322{txt}){col 67}= {res}     13.86
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}  Linearized
{col 1}tech_directionworse{col 21}{c |} Odds Ratio{col 33}   Std. Err.{col 45}      t{col 53}   P>|t|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}age_3group {c |}
{space 13}18-29  {c |}{col 21}{res}{space 2} .3723856{col 33}{space 2} .0710657{col 44}{space 1}   -5.18{col 53}{space 3}0.000{col 61}{space 4} .2560953{col 74}{space 3}  .541482
{txt}{space 13}30-49  {c |}{col 21}{res}{space 2} .6429063{col 33}{space 2} .0879312{col 44}{space 1}   -3.23{col 53}{space 3}0.001{col 61}{space 4} .4916103{col 74}{space 3} .8407645
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2} 1.581316{col 33}{space 2} .1725638{col 44}{space 1}    4.20{col 53}{space 3}0.000{col 61}{space 4}  1.27657{col 74}{space 3} 1.958811
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit tech_directionworse ib3.age_3 $controls_demographics if infullmodel_TableD2 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,324
{txt}{col 1}Number of PSUs{col 20}= {res}    1,324{txt}{col 49}Population size{col 67}={res} 1,363.3793
{txt}{col 49}Design df{col 67}= {res}     1,323
{txt}{col 49}F({res}  17{txt},{res}   1307{txt}){col 67}= {res}      5.64
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                 tech_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}age_3group {c |}
{space 30}18-29  {c |}{col 38}{res}{space 2} .4046574{col 50}{space 2} .0806054{col 61}{space 1}   -4.54{col 70}{space 3}0.000{col 78}{space 4}  .273765{col 91}{space 3} .5981322
{txt}{space 30}30-49  {c |}{col 38}{res}{space 2} .7862896{col 50}{space 2}  .117193{col 61}{space 1}   -1.61{col 70}{space 3}0.107{col 78}{space 4} .5869465{col 91}{space 3} 1.053335
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} 1.217073{col 50}{space 2}  .161402{col 61}{space 1}    1.48{col 70}{space 3}0.139{col 78}{space 4} .9382779{col 91}{space 3} 1.578709
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .9196671{col 50}{space 2} .1255281{col 61}{space 1}   -0.61{col 70}{space 3}0.540{col 78}{space 4} .7036249{col 91}{space 3} 1.202043
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.559233{col 50}{space 2} .2768503{col 61}{space 1}    2.50{col 70}{space 3}0.012{col 78}{space 4} 1.100619{col 91}{space 3} 2.208945
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.171102{col 50}{space 2} .1735922{col 61}{space 1}    1.07{col 70}{space 3}0.287{col 78}{space 4} .8755998{col 91}{space 3} 1.566331
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.160133{col 50}{space 2} .3559632{col 61}{space 1}    0.48{col 70}{space 3}0.628{col 78}{space 4} .6354692{col 91}{space 3} 2.117975
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9135928{col 50}{space 2} .2204282{col 61}{space 1}   -0.37{col 70}{space 3}0.708{col 78}{space 4} .5691018{col 91}{space 3} 1.466613
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.085455{col 50}{space 2} .4890278{col 61}{space 1}    0.18{col 70}{space 3}0.856{col 78}{space 4} .4485086{col 91}{space 3} 2.626958
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.938953{col 50}{space 2} .5305978{col 61}{space 1}    2.42{col 70}{space 3}0.016{col 78}{space 4}   1.1335{col 91}{space 3} 3.316755
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.783753{col 50}{space 2} .4656758{col 61}{space 1}    2.22{col 70}{space 3}0.027{col 78}{space 4} 1.068839{col 91}{space 3} 2.976853
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.612297{col 50}{space 2} .3969295{col 61}{space 1}    1.94{col 70}{space 3}0.053{col 78}{space 4} .9947108{col 91}{space 3} 2.613324
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.469768{col 50}{space 2} .3620708{col 61}{space 1}    1.56{col 70}{space 3}0.118{col 78}{space 4} .9064985{col 91}{space 3} 2.383036
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}  1.55857{col 50}{space 2} .2639934{col 61}{space 1}    2.62{col 70}{space 3}0.009{col 78}{space 4} 1.117934{col 91}{space 3} 2.172883
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 3.092465{col 50}{space 2} .6115659{col 61}{space 1}    5.71{col 70}{space 3}0.000{col 78}{space 4} 2.098056{col 91}{space 3} 4.558191
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 2.031276{col 50}{space 2} .5092101{col 61}{space 1}    2.83{col 70}{space 3}0.005{col 78}{space 4} 1.242196{col 91}{space 3} 3.321603
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 2.505983{col 50}{space 2} .6336438{col 61}{space 1}    3.63{col 70}{space 3}0.000{col 78}{space 4} 1.525993{col 91}{space 3} 4.115319
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .4926268{col 50}{space 2} .1383685{col 61}{space 1}   -2.52{col 70}{space 3}0.012{col 78}{space 4} .2839321{col 91}{space 3} .8547156
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit tech_directionworse ib3.age_3 $controls_demographics $controls_objective if infullmodel_TableD2 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,324
{txt}{col 1}Number of PSUs{col 20}= {res}    1,324{txt}{col 49}Population size{col 67}={res} 1,363.3793
{txt}{col 49}Design df{col 67}= {res}     1,323
{txt}{col 49}F({res}  23{txt},{res}   1301{txt}){col 67}= {res}      4.33
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                 tech_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}age_3group {c |}
{space 30}18-29  {c |}{col 38}{res}{space 2} .4137127{col 50}{space 2} .0822058{col 61}{space 1}   -4.44{col 70}{space 3}0.000{col 78}{space 4} .2801613{col 91}{space 3} .6109274
{txt}{space 30}30-49  {c |}{col 38}{res}{space 2} .8020971{col 50}{space 2} .1205424{col 61}{space 1}   -1.47{col 70}{space 3}0.143{col 78}{space 4} .5972934{col 91}{space 3} 1.077125
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} 1.183359{col 50}{space 2} .1598184{col 61}{space 1}    1.25{col 70}{space 3}0.213{col 78}{space 4} .9079297{col 91}{space 3} 1.542342
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .9272337{col 50}{space 2} .1274089{col 61}{space 1}   -0.55{col 70}{space 3}0.583{col 78}{space 4} .7081424{col 91}{space 3} 1.214109
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.554239{col 50}{space 2} .2761429{col 61}{space 1}    2.48{col 70}{space 3}0.013{col 78}{space 4} 1.096846{col 91}{space 3} 2.202369
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.211282{col 50}{space 2} .1837786{col 61}{space 1}    1.26{col 70}{space 3}0.207{col 78}{space 4} .8994571{col 91}{space 3} 1.631209
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.204785{col 50}{space 2} .3707448{col 61}{space 1}    0.61{col 70}{space 3}0.545{col 78}{space 4} .6587672{col 91}{space 3} 2.203368
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .8523103{col 50}{space 2} .2068088{col 61}{space 1}   -0.66{col 70}{space 3}0.510{col 78}{space 4} .5295036{col 91}{space 3} 1.371913
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.041189{col 50}{space 2} .4682696{col 61}{space 1}    0.09{col 70}{space 3}0.929{col 78}{space 4} .4308787{col 91}{space 3}  2.51596
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.921505{col 50}{space 2} .5230416{col 61}{space 1}    2.40{col 70}{space 3}0.017{col 78}{space 4} 1.126494{col 91}{space 3} 3.277587
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.825607{col 50}{space 2} .4733348{col 61}{space 1}    2.32{col 70}{space 3}0.020{col 78}{space 4} 1.097766{col 91}{space 3} 3.036022
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.655714{col 50}{space 2} .4066966{col 61}{space 1}    2.05{col 70}{space 3}0.040{col 78}{space 4} 1.022613{col 91}{space 3} 2.680768
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.503107{col 50}{space 2} .3684373{col 61}{space 1}    1.66{col 70}{space 3}0.097{col 78}{space 4} .9292974{col 91}{space 3} 2.431224
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.306476{col 50}{space 2} .2449688{col 61}{space 1}    1.43{col 70}{space 3}0.154{col 78}{space 4} .9043825{col 91}{space 3} 1.887343
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.758551{col 50}{space 2} .5149379{col 61}{space 1}    1.93{col 70}{space 3}0.054{col 78}{space 4} .9900981{col 91}{space 3} 3.123428
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.688526{col 50}{space 2} .4890938{col 61}{space 1}    1.81{col 70}{space 3}0.071{col 78}{space 4} .9565887{col 91}{space 3} 2.980508
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 2.096836{col 50}{space 2} .6928849{col 61}{space 1}    2.24{col 70}{space 3}0.025{col 78}{space 4} 1.096563{col 91}{space 3} 4.009551
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .8637467{col 50}{space 2} .1494429{col 61}{space 1}   -0.85{col 70}{space 3}0.397{col 78}{space 4} .6151474{col 91}{space 3} 1.212812
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} .8687686{col 50}{space 2} .3100844{col 61}{space 1}   -0.39{col 70}{space 3}0.694{col 78}{space 4}  .431332{col 91}{space 3} 1.749833
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} .9420662{col 50}{space 2} .3345992{col 61}{space 1}   -0.17{col 70}{space 3}0.867{col 78}{space 4} .4693299{col 91}{space 3}  1.89097
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2}  2.44301{col 50}{space 2} .8119485{col 61}{space 1}    2.69{col 70}{space 3}0.007{col 78}{space 4} 1.272812{col 91}{space 3} 4.689063
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.033409{col 50}{space 2} .2232271{col 61}{space 1}    0.15{col 70}{space 3}0.879{col 78}{space 4} .6764497{col 91}{space 3} 1.578734
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.559699{col 50}{space 2} .2770991{col 61}{space 1}    2.50{col 70}{space 3}0.012{col 78}{space 4} 1.100719{col 91}{space 3} 2.210067
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .5142009{col 50}{space 2} .1991241{col 61}{space 1}   -1.72{col 70}{space 3}0.086{col 78}{space 4} .2405489{col 91}{space 3} 1.099163
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. 
. ******* Gender ********
. 
. * Model 1: Worsen 
. svy: logit tech_worsen ib1.female_dummy if infullmodel_TableD2 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   1{txt},{res}   3962{txt}){col 67}= {res}      0.63
{txt}{col 49}Prob > F{col 67}= {res}    0.4268

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1} tech_worsen{col 14}{c |} Odds Ratio{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
female_dummy {c |}
{space 7}Male  {c |}{col 14}{res}{space 2} 1.075816{col 26}{space 2} .0989286{col 37}{space 1}    0.79{col 46}{space 3}0.427{col 54}{space 4} .8983395{col 67}{space 3} 1.288356
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .1987293{col 26}{space 2} .0120126{col 37}{space 1}  -26.73{col 46}{space 3}0.000{col 54}{space 4} .1765198{col 67}{space 3} .2237331
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit tech_worsen ib1.female_dummy $controls_demographics if infullmodel_TableD2 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      2.51
{txt}{col 49}Prob > F{col 67}= {res}    0.0006

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                         tech_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.153478{col 50}{space 2} .1128954{col 61}{space 1}    1.46{col 70}{space 3}0.145{col 78}{space 4} .9520797{col 91}{space 3} 1.397478
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.288334{col 50}{space 2} .1934766{col 61}{space 1}    1.69{col 70}{space 3}0.092{col 78}{space 4} .9597529{col 91}{space 3} 1.729408
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.262694{col 50}{space 2} .2013064{col 61}{space 1}    1.46{col 70}{space 3}0.144{col 78}{space 4} .9237462{col 91}{space 3}  1.72601
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.110653{col 50}{space 2} .1160975{col 61}{space 1}    1.00{col 70}{space 3}0.315{col 78}{space 4} .9048459{col 91}{space 3} 1.363272
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.259801{col 50}{space 2} .1461974{col 61}{space 1}    1.99{col 70}{space 3}0.047{col 78}{space 4}  1.00344{col 91}{space 3} 1.581657
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9847405{col 50}{space 2} .1058998{col 61}{space 1}   -0.14{col 70}{space 3}0.886{col 78}{space 4} .7975444{col 91}{space 3} 1.215874
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9241554{col 50}{space 2} .1945524{col 61}{space 1}   -0.37{col 70}{space 3}0.708{col 78}{space 4} .6116401{col 91}{space 3} 1.396349
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .8155661{col 50}{space 2} .1400316{col 61}{space 1}   -1.19{col 70}{space 3}0.235{col 78}{space 4} .5824577{col 91}{space 3} 1.141968
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .9556425{col 50}{space 2} .2910917{col 61}{space 1}   -0.15{col 70}{space 3}0.882{col 78}{space 4} .5259417{col 91}{space 3} 1.736414
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.167665{col 50}{space 2} .2125261{col 61}{space 1}    0.85{col 70}{space 3}0.394{col 78}{space 4} .8172294{col 91}{space 3}  1.66837
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.069154{col 50}{space 2}  .189471{col 61}{space 1}    0.38{col 70}{space 3}0.706{col 78}{space 4} .7553496{col 91}{space 3} 1.513327
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.117951{col 50}{space 2} .1984927{col 61}{space 1}    0.63{col 70}{space 3}0.530{col 78}{space 4} .7893058{col 91}{space 3} 1.583435
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.264681{col 50}{space 2}  .224145{col 61}{space 1}    1.32{col 70}{space 3}0.185{col 78}{space 4} .8934554{col 91}{space 3}  1.79015
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.282489{col 50}{space 2} .1698614{col 61}{space 1}    1.88{col 70}{space 3}0.060{col 78}{space 4} .9891918{col 91}{space 3}  1.66275
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.814161{col 50}{space 2} .2435089{col 61}{space 1}    4.44{col 70}{space 3}0.000{col 78}{space 4} 1.394398{col 91}{space 3} 2.360287
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .8573966{col 50}{space 2} .1444471{col 61}{space 1}   -0.91{col 70}{space 3}0.361{col 78}{space 4} .6162182{col 91}{space 3} 1.192968
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.127179{col 50}{space 2} .2059426{col 61}{space 1}    0.66{col 70}{space 3}0.512{col 78}{space 4} .7878174{col 91}{space 3} 1.612725
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .1082468{col 50}{space 2} .0233985{col 61}{space 1}  -10.29{col 70}{space 3}0.000{col 78}{space 4} .0708539{col 91}{space 3} .1653736
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit tech_worsen ib1.female_dummy $controls_demographics  $controls_objective if infullmodel_TableD2 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      2.57
{txt}{col 49}Prob > F{col 67}= {res}    0.0001

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                         tech_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.151599{col 50}{space 2} .1143805{col 61}{space 1}    1.42{col 70}{space 3}0.155{col 78}{space 4} .9478319{col 91}{space 3} 1.399172
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.316433{col 50}{space 2} .1963717{col 61}{space 1}    1.84{col 70}{space 3}0.065{col 78}{space 4}  .982622{col 91}{space 3} 1.763644
{txt}{space 32}50+  {c |}{col 38}{res}{space 2}  1.29173{col 50}{space 2} .2050114{col 61}{space 1}    1.61{col 70}{space 3}0.107{col 78}{space 4} .9463145{col 91}{space 3} 1.763225
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  1.10359{col 50}{space 2}  .116495{col 61}{space 1}    0.93{col 70}{space 3}0.350{col 78}{space 4} .8972786{col 91}{space 3} 1.357338
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.248167{col 50}{space 2} .1471494{col 61}{space 1}    1.88{col 70}{space 3}0.060{col 78}{space 4} .9905848{col 91}{space 3} 1.572728
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.037156{col 50}{space 2} .1137139{col 61}{space 1}    0.33{col 70}{space 3}0.739{col 78}{space 4} .8365462{col 91}{space 3} 1.285874
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9606719{col 50}{space 2} .2051961{col 61}{space 1}   -0.19{col 70}{space 3}0.851{col 78}{space 4} .6319838{col 91}{space 3} 1.460307
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .8244381{col 50}{space 2} .1417613{col 61}{space 1}   -1.12{col 70}{space 3}0.262{col 78}{space 4} .5885049{col 91}{space 3} 1.154958
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2}  .951978{col 50}{space 2} .2910022{col 61}{space 1}   -0.16{col 70}{space 3}0.872{col 78}{space 4} .5228182{col 91}{space 3} 1.733417
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.161317{col 50}{space 2} .2124477{col 61}{space 1}    0.82{col 70}{space 3}0.414{col 78}{space 4} .8113102{col 91}{space 3} 1.662319
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.074199{col 50}{space 2} .1910727{col 61}{space 1}    0.40{col 70}{space 3}0.687{col 78}{space 4} .7579338{col 91}{space 3} 1.522433
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.133929{col 50}{space 2} .2019321{col 61}{space 1}    0.71{col 70}{space 3}0.480{col 78}{space 4} .7997533{col 91}{space 3} 1.607739
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.255271{col 50}{space 2} .2217886{col 61}{space 1}    1.29{col 70}{space 3}0.198{col 78}{space 4} .8877618{col 91}{space 3}  1.77492
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.145882{col 50}{space 2} .1645278{col 61}{space 1}    0.95{col 70}{space 3}0.343{col 78}{space 4} .8647401{col 91}{space 3} 1.518428
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.295141{col 50}{space 2} .2747104{col 61}{space 1}    1.22{col 70}{space 3}0.223{col 78}{space 4}  .854505{col 91}{space 3} 1.962995
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .9488207{col 50}{space 2} .1787633{col 61}{space 1}   -0.28{col 70}{space 3}0.780{col 78}{space 4} .6557886{col 91}{space 3} 1.372791
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.531672{col 50}{space 2} .3715764{col 61}{space 1}    1.76{col 70}{space 3}0.079{col 78}{space 4} .9519308{col 91}{space 3} 2.464486
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .9881793{col 50}{space 2} .1211732{col 61}{space 1}   -0.10{col 70}{space 3}0.923{col 78}{space 4} .7770111{col 91}{space 3} 1.256737
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.529412{col 50}{space 2} .4135439{col 61}{space 1}    1.57{col 70}{space 3}0.116{col 78}{space 4} .9001072{col 91}{space 3} 2.598691
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.281864{col 50}{space 2} .3432878{col 61}{space 1}    0.93{col 70}{space 3}0.354{col 78}{space 4} .7582589{col 91}{space 3} 2.167038
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} 1.394314{col 50}{space 2} .3512092{col 61}{space 1}    1.32{col 70}{space 3}0.187{col 78}{space 4} .8509197{col 91}{space 3} 2.284717
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .8428629{col 50}{space 2} .1464735{col 61}{space 1}   -0.98{col 70}{space 3}0.325{col 78}{space 4} .5994999{col 91}{space 3} 1.185017
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.179514{col 50}{space 2} .1735202{col 61}{space 1}    1.12{col 70}{space 3}0.262{col 78}{space 4} .8839814{col 91}{space 3} 1.573849
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .0745967{col 50}{space 2}  .021244{col 61}{space 1}   -9.11{col 70}{space 3}0.000{col 78}{space 4} .0426812{col 91}{space 3} .1303775
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 2: Improve 
. svy: logit tech_improve ib1.female_dummy if infullmodel_TableD2 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   1{txt},{res}   3962{txt}){col 67}= {res}     29.08
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}tech_improve{col 14}{c |} Odds Ratio{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
female_dummy {c |}
{space 7}Male  {c |}{col 14}{res}{space 2} 1.656364{col 26}{space 2} .1550057{col 37}{space 1}    5.39{col 46}{space 3}0.000{col 54}{space 4} 1.378715{col 67}{space 3} 1.989927
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .1476911{col 26}{space 2} .0099743{col 37}{space 1}  -28.32{col 46}{space 3}0.000{col 54}{space 4} .1293753{col 67}{space 3}    .1686
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit tech_improve ib1.female_dummy $controls_demographics if infullmodel_TableD2 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}     10.37
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                        tech_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.514788{col 50}{space 2} .1540218{col 61}{space 1}    4.08{col 70}{space 3}0.000{col 78}{space 4} 1.241012{col 91}{space 3} 1.848959
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .5985055{col 50}{space 2} .0790013{col 61}{space 1}   -3.89{col 70}{space 3}0.000{col 78}{space 4} .4620376{col 91}{space 3} .7752808
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .4215797{col 50}{space 2} .0637814{col 61}{space 1}   -5.71{col 70}{space 3}0.000{col 78}{space 4} .3133725{col 91}{space 3} .5671507
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.265467{col 50}{space 2}  .133295{col 61}{space 1}    2.24{col 70}{space 3}0.025{col 78}{space 4} 1.029353{col 91}{space 3} 1.555742
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2}  .743296{col 50}{space 2} .1110058{col 61}{space 1}   -1.99{col 70}{space 3}0.047{col 78}{space 4} .5546286{col 91}{space 3} .9961423
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .7884021{col 50}{space 2} .0885944{col 61}{space 1}   -2.12{col 70}{space 3}0.034{col 78}{space 4} .6325097{col 91}{space 3} .9827167
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .7632024{col 50}{space 2} .1782472{col 61}{space 1}   -1.16{col 70}{space 3}0.247{col 78}{space 4} .4828136{col 91}{space 3} 1.206424
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9818739{col 50}{space 2}  .171683{col 61}{space 1}   -0.10{col 70}{space 3}0.917{col 78}{space 4} .6969083{col 91}{space 3} 1.383362
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8789707{col 50}{space 2} .3416891{col 61}{space 1}   -0.33{col 70}{space 3}0.740{col 78}{space 4} .4101844{col 91}{space 3} 1.883517
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5046275{col 50}{space 2} .1133756{col 61}{space 1}   -3.04{col 70}{space 3}0.002{col 78}{space 4} .3248415{col 91}{space 3} .7839176
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .5393284{col 50}{space 2} .1130688{col 61}{space 1}   -2.95{col 70}{space 3}0.003{col 78}{space 4}  .357558{col 91}{space 3} .8135046
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .6527976{col 50}{space 2} .1279229{col 61}{space 1}   -2.18{col 70}{space 3}0.030{col 78}{space 4} .4445547{col 91}{space 3} .9585877
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .8106742{col 50}{space 2} .1603535{col 61}{space 1}   -1.06{col 70}{space 3}0.289{col 78}{space 4} .5500789{col 91}{space 3} 1.194724
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}  .783422{col 50}{space 2} .1021823{col 61}{space 1}   -1.87{col 70}{space 3}0.061{col 78}{space 4} .6066511{col 91}{space 3} 1.011702
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} .4522395{col 50}{space 2}  .074826{col 61}{space 1}   -4.80{col 70}{space 3}0.000{col 78}{space 4} .3269555{col 91}{space 3} .6255304
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .4080237{col 50}{space 2} .0803554{col 61}{space 1}   -4.55{col 70}{space 3}0.000{col 78}{space 4} .2773321{col 91}{space 3} .6003032
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .4044302{col 50}{space 2}  .082221{col 61}{space 1}   -4.45{col 70}{space 3}0.000{col 78}{space 4} .2714817{col 91}{space 3} .6024853
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .5630686{col 50}{space 2} .1347651{col 61}{space 1}   -2.40{col 70}{space 3}0.016{col 78}{space 4}  .352186{col 91}{space 3}  .900224
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit tech_improve ib1.female_dummy $controls_demographics $controls_objective if infullmodel_TableD2 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      8.76
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                        tech_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.461056{col 50}{space 2}  .149884{col 61}{space 1}    3.70{col 70}{space 3}0.000{col 78}{space 4} 1.194865{col 91}{space 3} 1.786549
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .6059595{col 50}{space 2} .0801221{col 61}{space 1}   -3.79{col 70}{space 3}0.000{col 78}{space 4} .4675847{col 91}{space 3} .7852841
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .4297908{col 50}{space 2} .0649978{col 61}{space 1}   -5.58{col 70}{space 3}0.000{col 78}{space 4} .3195138{col 91}{space 3}  .578129
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.259845{col 50}{space 2}  .132008{col 61}{space 1}    2.20{col 70}{space 3}0.028{col 78}{space 4} 1.025888{col 91}{space 3} 1.547157
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .7404869{col 50}{space 2} .1100479{col 61}{space 1}   -2.02{col 70}{space 3}0.043{col 78}{space 4} .5533208{col 91}{space 3} .9909639
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .8021883{col 50}{space 2} .0926664{col 61}{space 1}   -1.91{col 70}{space 3}0.056{col 78}{space 4} .6396141{col 91}{space 3} 1.006085
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .7757171{col 50}{space 2} .1779182{col 61}{space 1}   -1.11{col 70}{space 3}0.268{col 78}{space 4} .4947803{col 91}{space 3}  1.21617
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.063545{col 50}{space 2} .1872545{col 61}{space 1}    0.35{col 70}{space 3}0.726{col 78}{space 4} .7530814{col 91}{space 3} 1.501998
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .9270945{col 50}{space 2} .3635164{col 61}{space 1}   -0.19{col 70}{space 3}0.847{col 78}{space 4} .4297971{col 91}{space 3} 1.999791
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5084147{col 50}{space 2} .1134948{col 61}{space 1}   -3.03{col 70}{space 3}0.002{col 78}{space 4} .3282041{col 91}{space 3} .7875756
{txt}{space 34}3  {c |}{col 38}{res}{space 2}  .526227{col 50}{space 2} .1103904{col 61}{space 1}   -3.06{col 70}{space 3}0.002{col 78}{space 4} .3487835{col 91}{space 3} .7939448
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .6499105{col 50}{space 2} .1271704{col 61}{space 1}   -2.20{col 70}{space 3}0.028{col 78}{space 4} .4428379{col 91}{space 3} .9538108
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .7725892{col 50}{space 2} .1535353{col 61}{space 1}   -1.30{col 70}{space 3}0.194{col 78}{space 4}  .523286{col 91}{space 3} 1.140665
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .8730948{col 50}{space 2} .1217765{col 61}{space 1}   -0.97{col 70}{space 3}0.331{col 78}{space 4} .6642051{col 91}{space 3} 1.147679
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} .6410654{col 50}{space 2} .1550765{col 61}{space 1}   -1.84{col 70}{space 3}0.066{col 78}{space 4} .3989607{col 91}{space 3} 1.030089
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .5559549{col 50}{space 2} .1282872{col 61}{space 1}   -2.54{col 70}{space 3}0.011{col 78}{space 4}  .353642{col 91}{space 3} .8740078
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .6599147{col 50}{space 2} .1820168{col 61}{space 1}   -1.51{col 70}{space 3}0.132{col 78}{space 4} .3842721{col 91}{space 3} 1.133279
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.274265{col 50}{space 2} .1638121{col 61}{space 1}    1.89{col 70}{space 3}0.059{col 78}{space 4} .9903772{col 91}{space 3} 1.639527
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.626424{col 50}{space 2}  .455619{col 61}{space 1}    1.74{col 70}{space 3}0.083{col 78}{space 4} .9390941{col 91}{space 3} 2.816815
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.166789{col 50}{space 2}  .320711{col 61}{space 1}    0.56{col 70}{space 3}0.575{col 78}{space 4} .6806977{col 91}{space 3} 2.000002
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .4380315{col 50}{space 2} .1188453{col 61}{space 1}   -3.04{col 70}{space 3}0.002{col 78}{space 4} .2573294{col 91}{space 3} .7456262
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .8297709{col 50}{space 2} .1214935{col 61}{space 1}   -1.27{col 70}{space 3}0.203{col 78}{space 4} .6227142{col 91}{space 3} 1.105675
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} .6009114{col 50}{space 2} .0793849{col 61}{space 1}   -3.86{col 70}{space 3}0.000{col 78}{space 4} .4637949{col 91}{space 3}  .778565
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .4216743{col 50}{space 2} .1323706{col 61}{space 1}   -2.75{col 70}{space 3}0.006{col 78}{space 4}  .227871{col 91}{space 3} .7803063
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 3: Worsen v Improve
. svy: logit tech_directionworse ib1.female_dummy if infullmodel_TableD2 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,324
{txt}{col 1}Number of PSUs{col 20}= {res}    1,324{txt}{col 49}Population size{col 67}={res} 1,363.3793
{txt}{col 49}Design df{col 67}= {res}     1,323
{txt}{col 49}F({res}   1{txt},{res}   1323{txt}){col 67}= {res}      9.20
{txt}{col 49}Prob > F{col 67}= {res}    0.0025

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}  Linearized
{col 1}tech_directionworse{col 21}{c |} Odds Ratio{col 33}   Std. Err.{col 45}      t{col 53}   P>|t|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}female_dummy {c |}
{space 14}Male  {c |}{col 21}{res}{space 2} .6956216{col 33}{space 2} .0832246{col 44}{space 1}   -3.03{col 53}{space 3}0.002{col 61}{space 4} .5500999{col 74}{space 3} .8796392
{txt}{space 14}_cons {c |}{col 21}{res}{space 2} 1.288283{col 33}{space 2} .1076748{col 44}{space 1}    3.03{col 53}{space 3}0.002{col 61}{space 4} 1.093459{col 74}{space 3} 1.517819
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit tech_directionworse ib1.female_dummy $controls_demographics if infullmodel_TableD2 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,324
{txt}{col 1}Number of PSUs{col 20}= {res}    1,324{txt}{col 49}Population size{col 67}={res} 1,363.3793
{txt}{col 49}Design df{col 67}= {res}     1,323
{txt}{col 49}F({res}  17{txt},{res}   1307{txt}){col 67}= {res}      5.64
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                 tech_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} .8216431{col 50}{space 2} .1089621{col 61}{space 1}   -1.48{col 70}{space 3}0.139{col 78}{space 4} .6334289{col 91}{space 3} 1.065782
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.943099{col 50}{space 2} .3551903{col 61}{space 1}    3.63{col 70}{space 3}0.000{col 78}{space 4} 1.357552{col 91}{space 3} 2.781208
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 2.471226{col 50}{space 2} .4922539{col 61}{space 1}    4.54{col 70}{space 3}0.000{col 78}{space 4} 1.671871{col 91}{space 3} 3.652768
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .9196671{col 50}{space 2} .1255281{col 61}{space 1}   -0.61{col 70}{space 3}0.540{col 78}{space 4} .7036249{col 91}{space 3} 1.202043
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.559233{col 50}{space 2} .2768503{col 61}{space 1}    2.50{col 70}{space 3}0.012{col 78}{space 4} 1.100619{col 91}{space 3} 2.208945
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.171102{col 50}{space 2} .1735922{col 61}{space 1}    1.07{col 70}{space 3}0.287{col 78}{space 4} .8755998{col 91}{space 3} 1.566331
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.160133{col 50}{space 2} .3559632{col 61}{space 1}    0.48{col 70}{space 3}0.628{col 78}{space 4} .6354692{col 91}{space 3} 2.117975
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9135928{col 50}{space 2} .2204282{col 61}{space 1}   -0.37{col 70}{space 3}0.708{col 78}{space 4} .5691018{col 91}{space 3} 1.466613
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.085455{col 50}{space 2} .4890278{col 61}{space 1}    0.18{col 70}{space 3}0.856{col 78}{space 4} .4485086{col 91}{space 3} 2.626958
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.938953{col 50}{space 2} .5305978{col 61}{space 1}    2.42{col 70}{space 3}0.016{col 78}{space 4}   1.1335{col 91}{space 3} 3.316755
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.783753{col 50}{space 2} .4656758{col 61}{space 1}    2.22{col 70}{space 3}0.027{col 78}{space 4} 1.068839{col 91}{space 3} 2.976853
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.612297{col 50}{space 2} .3969295{col 61}{space 1}    1.94{col 70}{space 3}0.053{col 78}{space 4} .9947108{col 91}{space 3} 2.613324
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.469768{col 50}{space 2} .3620708{col 61}{space 1}    1.56{col 70}{space 3}0.118{col 78}{space 4} .9064985{col 91}{space 3} 2.383036
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}  1.55857{col 50}{space 2} .2639934{col 61}{space 1}    2.62{col 70}{space 3}0.009{col 78}{space 4} 1.117934{col 91}{space 3} 2.172883
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 3.092465{col 50}{space 2} .6115659{col 61}{space 1}    5.71{col 70}{space 3}0.000{col 78}{space 4} 2.098056{col 91}{space 3} 4.558191
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 2.031276{col 50}{space 2} .5092101{col 61}{space 1}    2.83{col 70}{space 3}0.005{col 78}{space 4} 1.242196{col 91}{space 3} 3.321603
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 2.505983{col 50}{space 2} .6336438{col 61}{space 1}    3.63{col 70}{space 3}0.000{col 78}{space 4} 1.525993{col 91}{space 3} 4.115319
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2426176{col 50}{space 2} .0743045{col 61}{space 1}   -4.62{col 70}{space 3}0.000{col 78}{space 4} .1330433{col 91}{space 3} .4424371
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit tech_directionworse ib1.female_dummy $controls_demographics $controls_objective if infullmodel_TableD2 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,324
{txt}{col 1}Number of PSUs{col 20}= {res}    1,324{txt}{col 49}Population size{col 67}={res} 1,363.3793
{txt}{col 49}Design df{col 67}= {res}     1,323
{txt}{col 49}F({res}  23{txt},{res}   1301{txt}){col 67}= {res}      4.33
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                 tech_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} .8450523{col 50}{space 2} .1141284{col 61}{space 1}   -1.25{col 70}{space 3}0.213{col 78}{space 4} .6483647{col 91}{space 3} 1.101407
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.938778{col 50}{space 2} .3521668{col 61}{space 1}    3.64{col 70}{space 3}0.000{col 78}{space 4} 1.357598{col 91}{space 3} 2.768758
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 2.417136{col 50}{space 2} .4802911{col 61}{space 1}    4.44{col 70}{space 3}0.000{col 78}{space 4} 1.636856{col 91}{space 3} 3.569373
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .9272337{col 50}{space 2} .1274089{col 61}{space 1}   -0.55{col 70}{space 3}0.583{col 78}{space 4} .7081424{col 91}{space 3} 1.214109
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.554239{col 50}{space 2} .2761429{col 61}{space 1}    2.48{col 70}{space 3}0.013{col 78}{space 4} 1.096846{col 91}{space 3} 2.202369
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.211282{col 50}{space 2} .1837786{col 61}{space 1}    1.26{col 70}{space 3}0.207{col 78}{space 4} .8994571{col 91}{space 3} 1.631209
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.204785{col 50}{space 2} .3707448{col 61}{space 1}    0.61{col 70}{space 3}0.545{col 78}{space 4} .6587672{col 91}{space 3} 2.203368
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .8523103{col 50}{space 2} .2068088{col 61}{space 1}   -0.66{col 70}{space 3}0.510{col 78}{space 4} .5295036{col 91}{space 3} 1.371913
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.041189{col 50}{space 2} .4682696{col 61}{space 1}    0.09{col 70}{space 3}0.929{col 78}{space 4} .4308787{col 91}{space 3}  2.51596
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.921505{col 50}{space 2} .5230416{col 61}{space 1}    2.40{col 70}{space 3}0.017{col 78}{space 4} 1.126494{col 91}{space 3} 3.277587
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.825607{col 50}{space 2} .4733348{col 61}{space 1}    2.32{col 70}{space 3}0.020{col 78}{space 4} 1.097766{col 91}{space 3} 3.036022
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.655714{col 50}{space 2} .4066966{col 61}{space 1}    2.05{col 70}{space 3}0.040{col 78}{space 4} 1.022613{col 91}{space 3} 2.680768
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.503107{col 50}{space 2} .3684373{col 61}{space 1}    1.66{col 70}{space 3}0.097{col 78}{space 4} .9292974{col 91}{space 3} 2.431224
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.306476{col 50}{space 2} .2449688{col 61}{space 1}    1.43{col 70}{space 3}0.154{col 78}{space 4} .9043825{col 91}{space 3} 1.887343
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.758551{col 50}{space 2} .5149379{col 61}{space 1}    1.93{col 70}{space 3}0.054{col 78}{space 4} .9900981{col 91}{space 3} 3.123428
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.688526{col 50}{space 2} .4890938{col 61}{space 1}    1.81{col 70}{space 3}0.071{col 78}{space 4} .9565887{col 91}{space 3} 2.980508
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 2.096836{col 50}{space 2} .6928849{col 61}{space 1}    2.24{col 70}{space 3}0.025{col 78}{space 4} 1.096563{col 91}{space 3} 4.009551
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .8637467{col 50}{space 2} .1494429{col 61}{space 1}   -0.85{col 70}{space 3}0.397{col 78}{space 4} .6151474{col 91}{space 3} 1.212812
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} .8687686{col 50}{space 2} .3100844{col 61}{space 1}   -0.39{col 70}{space 3}0.694{col 78}{space 4}  .431332{col 91}{space 3} 1.749833
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} .9420662{col 50}{space 2} .3345992{col 61}{space 1}   -0.17{col 70}{space 3}0.867{col 78}{space 4} .4693299{col 91}{space 3}  1.89097
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2}  2.44301{col 50}{space 2} .8119485{col 61}{space 1}    2.69{col 70}{space 3}0.007{col 78}{space 4} 1.272812{col 91}{space 3} 4.689063
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.033409{col 50}{space 2} .2232271{col 61}{space 1}    0.15{col 70}{space 3}0.879{col 78}{space 4} .6764497{col 91}{space 3} 1.578734
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.559699{col 50}{space 2} .2770991{col 61}{space 1}    2.50{col 70}{space 3}0.012{col 78}{space 4} 1.100719{col 91}{space 3} 2.210067
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2517376{col 50}{space 2} .1009745{col 61}{space 1}   -3.44{col 70}{space 3}0.001{col 78}{space 4} .1146066{col 91}{space 3} .5529507
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. 
. ******* Education ********
. 
. * Model 1: Worsen 
. svy: logit tech_worsen i.degree_dummy if infullmodel_TableD2 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   1{txt},{res}   3962{txt}){col 67}= {res}      0.60
{txt}{col 49}Prob > F{col 67}= {res}    0.4390

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}  Linearized
{col 1}   tech_worsen{col 16}{c |} Odds Ratio{col 28}   Std. Err.{col 40}      t{col 48}   P>|t|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}degree_dummy {c |}
Degree-Holder  {c |}{col 16}{res}{space 2} 1.074147{col 28}{space 2} .0992612{col 39}{space 1}    0.77{col 48}{space 3}0.439{col 56}{space 4} .8961502{col 69}{space 3} 1.287499
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .1995897{col 28}{space 2} .0127177{col 39}{space 1}  -25.29{col 48}{space 3}0.000{col 56}{space 4} .1761505{col 69}{space 3} .2261479
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit tech_worsen i.degree_dummy $controls_demographics if infullmodel_TableD2 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      2.51
{txt}{col 49}Prob > F{col 67}= {res}    0.0006

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                         tech_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.110653{col 50}{space 2} .1160975{col 61}{space 1}    1.00{col 70}{space 3}0.315{col 78}{space 4} .9048459{col 91}{space 3} 1.363272
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.288334{col 50}{space 2} .1934766{col 61}{space 1}    1.69{col 70}{space 3}0.092{col 78}{space 4} .9597529{col 91}{space 3} 1.729408
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.262694{col 50}{space 2} .2013064{col 61}{space 1}    1.46{col 70}{space 3}0.144{col 78}{space 4} .9237462{col 91}{space 3}  1.72601
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .8669435{col 50}{space 2} .0848512{col 61}{space 1}   -1.46{col 70}{space 3}0.145{col 78}{space 4} .7155746{col 91}{space 3} 1.050332
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.259801{col 50}{space 2} .1461974{col 61}{space 1}    1.99{col 70}{space 3}0.047{col 78}{space 4}  1.00344{col 91}{space 3} 1.581657
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9847405{col 50}{space 2} .1058998{col 61}{space 1}   -0.14{col 70}{space 3}0.886{col 78}{space 4} .7975444{col 91}{space 3} 1.215874
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9241554{col 50}{space 2} .1945524{col 61}{space 1}   -0.37{col 70}{space 3}0.708{col 78}{space 4} .6116401{col 91}{space 3} 1.396349
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .8155661{col 50}{space 2} .1400316{col 61}{space 1}   -1.19{col 70}{space 3}0.235{col 78}{space 4} .5824577{col 91}{space 3} 1.141968
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .9556425{col 50}{space 2} .2910917{col 61}{space 1}   -0.15{col 70}{space 3}0.882{col 78}{space 4} .5259417{col 91}{space 3} 1.736414
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.167665{col 50}{space 2} .2125261{col 61}{space 1}    0.85{col 70}{space 3}0.394{col 78}{space 4} .8172294{col 91}{space 3}  1.66837
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.069154{col 50}{space 2}  .189471{col 61}{space 1}    0.38{col 70}{space 3}0.706{col 78}{space 4} .7553496{col 91}{space 3} 1.513327
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.117951{col 50}{space 2} .1984927{col 61}{space 1}    0.63{col 70}{space 3}0.530{col 78}{space 4} .7893058{col 91}{space 3} 1.583435
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.264681{col 50}{space 2}  .224145{col 61}{space 1}    1.32{col 70}{space 3}0.185{col 78}{space 4} .8934554{col 91}{space 3}  1.79015
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.282489{col 50}{space 2} .1698614{col 61}{space 1}    1.88{col 70}{space 3}0.060{col 78}{space 4} .9891918{col 91}{space 3}  1.66275
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.814161{col 50}{space 2} .2435089{col 61}{space 1}    4.44{col 70}{space 3}0.000{col 78}{space 4} 1.394398{col 91}{space 3} 2.360287
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .8573966{col 50}{space 2} .1444471{col 61}{space 1}   -0.91{col 70}{space 3}0.361{col 78}{space 4} .6162182{col 91}{space 3} 1.192968
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.127179{col 50}{space 2} .2059426{col 61}{space 1}    0.66{col 70}{space 3}0.512{col 78}{space 4} .7878174{col 91}{space 3} 1.612725
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .1248602{col 50}{space 2} .0272465{col 61}{space 1}   -9.53{col 70}{space 3}0.000{col 78}{space 4} .0813994{col 91}{space 3} .1915256
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit tech_worsen i.degree_dummy $controls_demographics  $controls_objective if infullmodel_TableD2 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      2.57
{txt}{col 49}Prob > F{col 67}= {res}    0.0001

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                         tech_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  1.10359{col 50}{space 2}  .116495{col 61}{space 1}    0.93{col 70}{space 3}0.350{col 78}{space 4} .8972786{col 91}{space 3} 1.357338
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.316433{col 50}{space 2} .1963717{col 61}{space 1}    1.84{col 70}{space 3}0.065{col 78}{space 4}  .982622{col 91}{space 3} 1.763644
{txt}{space 32}50+  {c |}{col 38}{res}{space 2}  1.29173{col 50}{space 2} .2050114{col 61}{space 1}    1.61{col 70}{space 3}0.107{col 78}{space 4} .9463145{col 91}{space 3} 1.763225
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}  .868358{col 50}{space 2} .0862481{col 61}{space 1}   -1.42{col 70}{space 3}0.155{col 78}{space 4} .7147084{col 91}{space 3} 1.055039
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.248167{col 50}{space 2} .1471494{col 61}{space 1}    1.88{col 70}{space 3}0.060{col 78}{space 4} .9905848{col 91}{space 3} 1.572728
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.037156{col 50}{space 2} .1137139{col 61}{space 1}    0.33{col 70}{space 3}0.739{col 78}{space 4} .8365462{col 91}{space 3} 1.285874
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9606719{col 50}{space 2} .2051961{col 61}{space 1}   -0.19{col 70}{space 3}0.851{col 78}{space 4} .6319838{col 91}{space 3} 1.460307
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .8244381{col 50}{space 2} .1417613{col 61}{space 1}   -1.12{col 70}{space 3}0.262{col 78}{space 4} .5885049{col 91}{space 3} 1.154958
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2}  .951978{col 50}{space 2} .2910022{col 61}{space 1}   -0.16{col 70}{space 3}0.872{col 78}{space 4} .5228182{col 91}{space 3} 1.733417
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.161317{col 50}{space 2} .2124477{col 61}{space 1}    0.82{col 70}{space 3}0.414{col 78}{space 4} .8113102{col 91}{space 3} 1.662319
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.074199{col 50}{space 2} .1910727{col 61}{space 1}    0.40{col 70}{space 3}0.687{col 78}{space 4} .7579338{col 91}{space 3} 1.522433
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.133929{col 50}{space 2} .2019321{col 61}{space 1}    0.71{col 70}{space 3}0.480{col 78}{space 4} .7997533{col 91}{space 3} 1.607739
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.255271{col 50}{space 2} .2217886{col 61}{space 1}    1.29{col 70}{space 3}0.198{col 78}{space 4} .8877618{col 91}{space 3}  1.77492
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.145882{col 50}{space 2} .1645278{col 61}{space 1}    0.95{col 70}{space 3}0.343{col 78}{space 4} .8647401{col 91}{space 3} 1.518428
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.295141{col 50}{space 2} .2747104{col 61}{space 1}    1.22{col 70}{space 3}0.223{col 78}{space 4}  .854505{col 91}{space 3} 1.962995
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .9488207{col 50}{space 2} .1787633{col 61}{space 1}   -0.28{col 70}{space 3}0.780{col 78}{space 4} .6557886{col 91}{space 3} 1.372791
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.531672{col 50}{space 2} .3715764{col 61}{space 1}    1.76{col 70}{space 3}0.079{col 78}{space 4} .9519308{col 91}{space 3} 2.464486
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .9881793{col 50}{space 2} .1211732{col 61}{space 1}   -0.10{col 70}{space 3}0.923{col 78}{space 4} .7770111{col 91}{space 3} 1.256737
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.529412{col 50}{space 2} .4135439{col 61}{space 1}    1.57{col 70}{space 3}0.116{col 78}{space 4} .9001072{col 91}{space 3} 2.598691
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.281864{col 50}{space 2} .3432878{col 61}{space 1}    0.93{col 70}{space 3}0.354{col 78}{space 4} .7582589{col 91}{space 3} 2.167038
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} 1.394314{col 50}{space 2} .3512092{col 61}{space 1}    1.32{col 70}{space 3}0.187{col 78}{space 4} .8509197{col 91}{space 3} 2.284717
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .8428629{col 50}{space 2} .1464735{col 61}{space 1}   -0.98{col 70}{space 3}0.325{col 78}{space 4} .5994999{col 91}{space 3} 1.185017
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.179514{col 50}{space 2} .1735202{col 61}{space 1}    1.12{col 70}{space 3}0.262{col 78}{space 4} .8839814{col 91}{space 3} 1.573849
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .0859055{col 50}{space 2} .0244577{col 61}{space 1}   -8.62{col 70}{space 3}0.000{col 78}{space 4} .0491593{col 91}{space 3}  .150119
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 2: Improve 
. svy: logit tech_improve i.degree_dummy if infullmodel_TableD2 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   1{txt},{res}   3962{txt}){col 67}= {res}     38.01
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}  Linearized
{col 1}  tech_improve{col 16}{c |} Odds Ratio{col 28}   Std. Err.{col 40}      t{col 48}   P>|t|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}degree_dummy {c |}
Degree-Holder  {c |}{col 16}{res}{space 2} 1.803564{col 28}{space 2} .1725283{col 39}{space 1}    6.17{col 48}{space 3}0.000{col 56}{space 4} 1.495137{col 69}{space 3} 2.175615
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .1445763{col 28}{space 2} .0105435{col 39}{space 1}  -26.52{col 48}{space 3}0.000{col 56}{space 4}  .125315{col 69}{space 3} .1667982
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit tech_improve i.degree_dummy $controls_demographics if infullmodel_TableD2 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}     10.37
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                        tech_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.265467{col 50}{space 2}  .133295{col 61}{space 1}    2.24{col 70}{space 3}0.025{col 78}{space 4} 1.029353{col 91}{space 3} 1.555742
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .5985055{col 50}{space 2} .0790013{col 61}{space 1}   -3.89{col 70}{space 3}0.000{col 78}{space 4} .4620376{col 91}{space 3} .7752808
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .4215797{col 50}{space 2} .0637814{col 61}{space 1}   -5.71{col 70}{space 3}0.000{col 78}{space 4} .3133725{col 91}{space 3} .5671507
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6601585{col 50}{space 2} .0671241{col 61}{space 1}   -4.08{col 70}{space 3}0.000{col 78}{space 4} .5408448{col 91}{space 3} .8057937
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2}  .743296{col 50}{space 2} .1110058{col 61}{space 1}   -1.99{col 70}{space 3}0.047{col 78}{space 4} .5546286{col 91}{space 3} .9961423
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .7884021{col 50}{space 2} .0885944{col 61}{space 1}   -2.12{col 70}{space 3}0.034{col 78}{space 4} .6325097{col 91}{space 3} .9827167
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .7632024{col 50}{space 2} .1782472{col 61}{space 1}   -1.16{col 70}{space 3}0.247{col 78}{space 4} .4828136{col 91}{space 3} 1.206424
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9818739{col 50}{space 2}  .171683{col 61}{space 1}   -0.10{col 70}{space 3}0.917{col 78}{space 4} .6969083{col 91}{space 3} 1.383362
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8789707{col 50}{space 2} .3416891{col 61}{space 1}   -0.33{col 70}{space 3}0.740{col 78}{space 4} .4101844{col 91}{space 3} 1.883517
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5046275{col 50}{space 2} .1133756{col 61}{space 1}   -3.04{col 70}{space 3}0.002{col 78}{space 4} .3248415{col 91}{space 3} .7839176
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .5393284{col 50}{space 2} .1130688{col 61}{space 1}   -2.95{col 70}{space 3}0.003{col 78}{space 4}  .357558{col 91}{space 3} .8135046
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .6527976{col 50}{space 2} .1279229{col 61}{space 1}   -2.18{col 70}{space 3}0.030{col 78}{space 4} .4445547{col 91}{space 3} .9585877
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .8106742{col 50}{space 2} .1603535{col 61}{space 1}   -1.06{col 70}{space 3}0.289{col 78}{space 4} .5500789{col 91}{space 3} 1.194724
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}  .783422{col 50}{space 2} .1021823{col 61}{space 1}   -1.87{col 70}{space 3}0.061{col 78}{space 4} .6066511{col 91}{space 3} 1.011702
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} .4522395{col 50}{space 2}  .074826{col 61}{space 1}   -4.80{col 70}{space 3}0.000{col 78}{space 4} .3269555{col 91}{space 3} .6255304
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .4080237{col 50}{space 2} .0803554{col 61}{space 1}   -4.55{col 70}{space 3}0.000{col 78}{space 4} .2773321{col 91}{space 3} .6003032
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .4044302{col 50}{space 2}  .082221{col 61}{space 1}   -4.45{col 70}{space 3}0.000{col 78}{space 4} .2714817{col 91}{space 3} .6024853
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .8529294{col 50}{space 2} .2005401{col 61}{space 1}   -0.68{col 70}{space 3}0.499{col 78}{space 4} .5379205{col 91}{space 3} 1.352409
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit tech_improve i.degree_dummy $controls_demographics $controls_objective if infullmodel_TableD2 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      8.76
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                        tech_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.259845{col 50}{space 2}  .132008{col 61}{space 1}    2.20{col 70}{space 3}0.028{col 78}{space 4} 1.025888{col 91}{space 3} 1.547157
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .6059595{col 50}{space 2} .0801221{col 61}{space 1}   -3.79{col 70}{space 3}0.000{col 78}{space 4} .4675847{col 91}{space 3} .7852841
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .4297908{col 50}{space 2} .0649978{col 61}{space 1}   -5.58{col 70}{space 3}0.000{col 78}{space 4} .3195138{col 91}{space 3}  .578129
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6844365{col 50}{space 2} .0702137{col 61}{space 1}   -3.70{col 70}{space 3}0.000{col 78}{space 4} .5597383{col 91}{space 3} .8369149
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .7404869{col 50}{space 2} .1100479{col 61}{space 1}   -2.02{col 70}{space 3}0.043{col 78}{space 4} .5533208{col 91}{space 3} .9909639
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .8021883{col 50}{space 2} .0926664{col 61}{space 1}   -1.91{col 70}{space 3}0.056{col 78}{space 4} .6396141{col 91}{space 3} 1.006085
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .7757171{col 50}{space 2} .1779182{col 61}{space 1}   -1.11{col 70}{space 3}0.268{col 78}{space 4} .4947803{col 91}{space 3}  1.21617
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.063545{col 50}{space 2} .1872545{col 61}{space 1}    0.35{col 70}{space 3}0.726{col 78}{space 4} .7530814{col 91}{space 3} 1.501998
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .9270945{col 50}{space 2} .3635164{col 61}{space 1}   -0.19{col 70}{space 3}0.847{col 78}{space 4} .4297971{col 91}{space 3} 1.999791
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5084147{col 50}{space 2} .1134948{col 61}{space 1}   -3.03{col 70}{space 3}0.002{col 78}{space 4} .3282041{col 91}{space 3} .7875756
{txt}{space 34}3  {c |}{col 38}{res}{space 2}  .526227{col 50}{space 2} .1103904{col 61}{space 1}   -3.06{col 70}{space 3}0.002{col 78}{space 4} .3487835{col 91}{space 3} .7939448
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .6499105{col 50}{space 2} .1271704{col 61}{space 1}   -2.20{col 70}{space 3}0.028{col 78}{space 4} .4428379{col 91}{space 3} .9538108
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .7725892{col 50}{space 2} .1535353{col 61}{space 1}   -1.30{col 70}{space 3}0.194{col 78}{space 4}  .523286{col 91}{space 3} 1.140665
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .8730948{col 50}{space 2} .1217765{col 61}{space 1}   -0.97{col 70}{space 3}0.331{col 78}{space 4} .6642051{col 91}{space 3} 1.147679
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} .6410654{col 50}{space 2} .1550765{col 61}{space 1}   -1.84{col 70}{space 3}0.066{col 78}{space 4} .3989607{col 91}{space 3} 1.030089
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .5559549{col 50}{space 2} .1282872{col 61}{space 1}   -2.54{col 70}{space 3}0.011{col 78}{space 4}  .353642{col 91}{space 3} .8740078
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .6599147{col 50}{space 2} .1820168{col 61}{space 1}   -1.51{col 70}{space 3}0.132{col 78}{space 4} .3842721{col 91}{space 3} 1.133279
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.274265{col 50}{space 2} .1638121{col 61}{space 1}    1.89{col 70}{space 3}0.059{col 78}{space 4} .9903772{col 91}{space 3} 1.639527
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.626424{col 50}{space 2}  .455619{col 61}{space 1}    1.74{col 70}{space 3}0.083{col 78}{space 4} .9390941{col 91}{space 3} 2.816815
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.166789{col 50}{space 2}  .320711{col 61}{space 1}    0.56{col 70}{space 3}0.575{col 78}{space 4} .6806977{col 91}{space 3} 2.000002
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .4380315{col 50}{space 2} .1188453{col 61}{space 1}   -3.04{col 70}{space 3}0.002{col 78}{space 4} .2573294{col 91}{space 3} .7456262
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .8297709{col 50}{space 2} .1214935{col 61}{space 1}   -1.27{col 70}{space 3}0.203{col 78}{space 4} .6227142{col 91}{space 3} 1.105675
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} .6009114{col 50}{space 2} .0793849{col 61}{space 1}   -3.86{col 70}{space 3}0.000{col 78}{space 4} .4637949{col 91}{space 3}  .778565
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .6160897{col 50}{space 2} .1882486{col 61}{space 1}   -1.59{col 70}{space 3}0.113{col 78}{space 4}  .338436{col 91}{space 3} 1.121531
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 3: Worsen v Improve
. svy: logit tech_directionworse i.degree_dummy if infullmodel_TableD2 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,324
{txt}{col 1}Number of PSUs{col 20}= {res}    1,324{txt}{col 49}Population size{col 67}={res} 1,363.3793
{txt}{col 49}Design df{col 67}= {res}     1,323
{txt}{col 49}F({res}   1{txt},{res}   1323{txt}){col 67}= {res}     12.73
{txt}{col 49}Prob > F{col 67}= {res}    0.0004

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}  Linearized
{col 1}tech_directionworse{col 21}{c |} Odds Ratio{col 33}   Std. Err.{col 45}      t{col 53}   P>|t|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}degree_dummy {c |}
{space 5}Degree-Holder  {c |}{col 21}{res}{space 2} .6480258{col 33}{space 2}  .078787{col 44}{space 1}   -3.57{col 53}{space 3}0.000{col 61}{space 4}  .510515{col 74}{space 3} .8225763
{txt}{space 14}_cons {c |}{col 21}{res}{space 2} 1.317204{col 33}{space 2} .1183116{col 44}{space 1}    3.07{col 53}{space 3}0.002{col 61}{space 4} 1.104404{col 74}{space 3} 1.571007
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit tech_directionworse i.degree_dummy $controls_demographics if infullmodel_TableD2 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,324
{txt}{col 1}Number of PSUs{col 20}= {res}    1,324{txt}{col 49}Population size{col 67}={res} 1,363.3793
{txt}{col 49}Design df{col 67}= {res}     1,323
{txt}{col 49}F({res}  17{txt},{res}   1307{txt}){col 67}= {res}      5.64
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                 tech_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .9196671{col 50}{space 2} .1255281{col 61}{space 1}   -0.61{col 70}{space 3}0.540{col 78}{space 4} .7036249{col 91}{space 3} 1.202043
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.943099{col 50}{space 2} .3551903{col 61}{space 1}    3.63{col 70}{space 3}0.000{col 78}{space 4} 1.357552{col 91}{space 3} 2.781208
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 2.471226{col 50}{space 2} .4922539{col 61}{space 1}    4.54{col 70}{space 3}0.000{col 78}{space 4} 1.671871{col 91}{space 3} 3.652768
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} 1.217073{col 50}{space 2}  .161402{col 61}{space 1}    1.48{col 70}{space 3}0.139{col 78}{space 4} .9382779{col 91}{space 3} 1.578709
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.559233{col 50}{space 2} .2768503{col 61}{space 1}    2.50{col 70}{space 3}0.012{col 78}{space 4} 1.100619{col 91}{space 3} 2.208945
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.171102{col 50}{space 2} .1735922{col 61}{space 1}    1.07{col 70}{space 3}0.287{col 78}{space 4} .8755998{col 91}{space 3} 1.566331
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.160133{col 50}{space 2} .3559632{col 61}{space 1}    0.48{col 70}{space 3}0.628{col 78}{space 4} .6354692{col 91}{space 3} 2.117975
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9135928{col 50}{space 2} .2204282{col 61}{space 1}   -0.37{col 70}{space 3}0.708{col 78}{space 4} .5691018{col 91}{space 3} 1.466613
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.085455{col 50}{space 2} .4890278{col 61}{space 1}    0.18{col 70}{space 3}0.856{col 78}{space 4} .4485086{col 91}{space 3} 2.626958
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.938953{col 50}{space 2} .5305978{col 61}{space 1}    2.42{col 70}{space 3}0.016{col 78}{space 4}   1.1335{col 91}{space 3} 3.316755
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.783753{col 50}{space 2} .4656758{col 61}{space 1}    2.22{col 70}{space 3}0.027{col 78}{space 4} 1.068839{col 91}{space 3} 2.976853
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.612297{col 50}{space 2} .3969295{col 61}{space 1}    1.94{col 70}{space 3}0.053{col 78}{space 4} .9947108{col 91}{space 3} 2.613324
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.469768{col 50}{space 2} .3620708{col 61}{space 1}    1.56{col 70}{space 3}0.118{col 78}{space 4} .9064985{col 91}{space 3} 2.383036
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}  1.55857{col 50}{space 2} .2639934{col 61}{space 1}    2.62{col 70}{space 3}0.009{col 78}{space 4} 1.117934{col 91}{space 3} 2.172883
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 3.092465{col 50}{space 2} .6115659{col 61}{space 1}    5.71{col 70}{space 3}0.000{col 78}{space 4} 2.098056{col 91}{space 3} 4.558191
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 2.031276{col 50}{space 2} .5092101{col 61}{space 1}    2.83{col 70}{space 3}0.005{col 78}{space 4} 1.242196{col 91}{space 3} 3.321603
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 2.505983{col 50}{space 2} .6336438{col 61}{space 1}    3.63{col 70}{space 3}0.000{col 78}{space 4} 1.525993{col 91}{space 3} 4.115319
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .1993451{col 50}{space 2} .0601458{col 61}{space 1}   -5.35{col 70}{space 3}0.000{col 78}{space 4} .1102931{col 91}{space 3} .3602988
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit tech_directionworse i.degree_dummy $controls_demographics $controls_objective if infullmodel_TableD2 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,324
{txt}{col 1}Number of PSUs{col 20}= {res}    1,324{txt}{col 49}Population size{col 67}={res} 1,363.3793
{txt}{col 49}Design df{col 67}= {res}     1,323
{txt}{col 49}F({res}  23{txt},{res}   1301{txt}){col 67}= {res}      4.33
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                 tech_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .9272337{col 50}{space 2} .1274089{col 61}{space 1}   -0.55{col 70}{space 3}0.583{col 78}{space 4} .7081424{col 91}{space 3} 1.214109
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.938778{col 50}{space 2} .3521668{col 61}{space 1}    3.64{col 70}{space 3}0.000{col 78}{space 4} 1.357598{col 91}{space 3} 2.768758
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 2.417136{col 50}{space 2} .4802911{col 61}{space 1}    4.44{col 70}{space 3}0.000{col 78}{space 4} 1.636856{col 91}{space 3} 3.569373
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} 1.183359{col 50}{space 2} .1598184{col 61}{space 1}    1.25{col 70}{space 3}0.213{col 78}{space 4} .9079297{col 91}{space 3} 1.542342
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.554239{col 50}{space 2} .2761429{col 61}{space 1}    2.48{col 70}{space 3}0.013{col 78}{space 4} 1.096846{col 91}{space 3} 2.202369
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.211282{col 50}{space 2} .1837786{col 61}{space 1}    1.26{col 70}{space 3}0.207{col 78}{space 4} .8994571{col 91}{space 3} 1.631209
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.204785{col 50}{space 2} .3707448{col 61}{space 1}    0.61{col 70}{space 3}0.545{col 78}{space 4} .6587672{col 91}{space 3} 2.203368
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .8523103{col 50}{space 2} .2068088{col 61}{space 1}   -0.66{col 70}{space 3}0.510{col 78}{space 4} .5295036{col 91}{space 3} 1.371913
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.041189{col 50}{space 2} .4682696{col 61}{space 1}    0.09{col 70}{space 3}0.929{col 78}{space 4} .4308787{col 91}{space 3}  2.51596
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.921505{col 50}{space 2} .5230416{col 61}{space 1}    2.40{col 70}{space 3}0.017{col 78}{space 4} 1.126494{col 91}{space 3} 3.277587
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.825607{col 50}{space 2} .4733348{col 61}{space 1}    2.32{col 70}{space 3}0.020{col 78}{space 4} 1.097766{col 91}{space 3} 3.036022
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.655714{col 50}{space 2} .4066966{col 61}{space 1}    2.05{col 70}{space 3}0.040{col 78}{space 4} 1.022613{col 91}{space 3} 2.680768
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.503107{col 50}{space 2} .3684373{col 61}{space 1}    1.66{col 70}{space 3}0.097{col 78}{space 4} .9292974{col 91}{space 3} 2.431224
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.306476{col 50}{space 2} .2449688{col 61}{space 1}    1.43{col 70}{space 3}0.154{col 78}{space 4} .9043825{col 91}{space 3} 1.887343
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.758551{col 50}{space 2} .5149379{col 61}{space 1}    1.93{col 70}{space 3}0.054{col 78}{space 4} .9900981{col 91}{space 3} 3.123428
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.688526{col 50}{space 2} .4890938{col 61}{space 1}    1.81{col 70}{space 3}0.071{col 78}{space 4} .9565887{col 91}{space 3} 2.980508
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 2.096836{col 50}{space 2} .6928849{col 61}{space 1}    2.24{col 70}{space 3}0.025{col 78}{space 4} 1.096563{col 91}{space 3} 4.009551
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .8637467{col 50}{space 2} .1494429{col 61}{space 1}   -0.85{col 70}{space 3}0.397{col 78}{space 4} .6151474{col 91}{space 3} 1.212812
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} .8687686{col 50}{space 2} .3100844{col 61}{space 1}   -0.39{col 70}{space 3}0.694{col 78}{space 4}  .431332{col 91}{space 3} 1.749833
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} .9420662{col 50}{space 2} .3345992{col 61}{space 1}   -0.17{col 70}{space 3}0.867{col 78}{space 4} .4693299{col 91}{space 3}  1.89097
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2}  2.44301{col 50}{space 2} .8119485{col 61}{space 1}    2.69{col 70}{space 3}0.007{col 78}{space 4} 1.272812{col 91}{space 3} 4.689063
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.033409{col 50}{space 2} .2232271{col 61}{space 1}    0.15{col 70}{space 3}0.879{col 78}{space 4} .6764497{col 91}{space 3} 1.578734
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.559699{col 50}{space 2} .2770991{col 61}{space 1}    2.50{col 70}{space 3}0.012{col 78}{space 4} 1.100719{col 91}{space 3} 2.210067
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2127314{col 50}{space 2} .0828209{col 61}{space 1}   -3.98{col 70}{space 3}0.000{col 78}{space 4} .0991145{col 91}{space 3} .4565899
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. 
. ******* ISCO-08 Occupational Group ********
. 
. * Model 1: Worsen 
. svy: logit tech_worsen i.isco08_5group if infullmodel_TableD2 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   4{txt},{res}   3959{txt}){col 67}= {res}      7.20
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                       tech_worsen{col 36}{c |} Odds Ratio{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}isco08_5group {c |}
Technician/Associate Professional  {c |}{col 36}{res}{space 2} 1.232658{col 48}{space 2} .1600395{col 59}{space 1}    1.61{col 68}{space 3}0.107{col 76}{space 4} .9556409{col 89}{space 3} 1.589975
{txt}{space 9}Clerical Support Workers  {c |}{col 36}{res}{space 2} 1.715449{col 48}{space 2} .2119906{col 59}{space 1}    4.37{col 68}{space 3}0.000{col 76}{space 4} 1.346345{col 89}{space 3} 2.185744
{txt}{space 8}Service and Sales Workers  {c |}{col 36}{res}{space 2} .7861778{col 48}{space 2} .1230236{col 59}{space 1}   -1.54{col 68}{space 3}0.124{col 76}{space 4} .5784707{col 89}{space 3} 1.068465
{txt}{space 13}Manual Working Class  {c |}{col 36}{res}{space 2} 1.071532{col 48}{space 2} .1731875{col 59}{space 1}    0.43{col 68}{space 3}0.669{col 76}{space 4} .7805246{col 89}{space 3} 1.471037
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .1846443{col 48}{space 2} .0141723{col 59}{space 1}  -22.01{col 68}{space 3}0.000{col 76}{space 4} .1588481{col 89}{space 3} .2146296
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit tech_worsen i.isco08_5group $controls_demographics if infullmodel_TableD2 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      2.51
{txt}{col 49}Prob > F{col 67}= {res}    0.0006

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                         tech_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.282489{col 50}{space 2} .1698614{col 61}{space 1}    1.88{col 70}{space 3}0.060{col 78}{space 4} .9891918{col 91}{space 3}  1.66275
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.814161{col 50}{space 2} .2435089{col 61}{space 1}    4.44{col 70}{space 3}0.000{col 78}{space 4} 1.394398{col 91}{space 3} 2.360287
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .8573966{col 50}{space 2} .1444471{col 61}{space 1}   -0.91{col 70}{space 3}0.361{col 78}{space 4} .6162182{col 91}{space 3} 1.192968
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.127179{col 50}{space 2} .2059426{col 61}{space 1}    0.66{col 70}{space 3}0.512{col 78}{space 4} .7878174{col 91}{space 3} 1.612725
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.288334{col 50}{space 2} .1934766{col 61}{space 1}    1.69{col 70}{space 3}0.092{col 78}{space 4} .9597529{col 91}{space 3} 1.729408
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.262694{col 50}{space 2} .2013064{col 61}{space 1}    1.46{col 70}{space 3}0.144{col 78}{space 4} .9237462{col 91}{space 3}  1.72601
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .8669435{col 50}{space 2} .0848512{col 61}{space 1}   -1.46{col 70}{space 3}0.145{col 78}{space 4} .7155746{col 91}{space 3} 1.050332
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.110653{col 50}{space 2} .1160975{col 61}{space 1}    1.00{col 70}{space 3}0.315{col 78}{space 4} .9048459{col 91}{space 3} 1.363272
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.259801{col 50}{space 2} .1461974{col 61}{space 1}    1.99{col 70}{space 3}0.047{col 78}{space 4}  1.00344{col 91}{space 3} 1.581657
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9847405{col 50}{space 2} .1058998{col 61}{space 1}   -0.14{col 70}{space 3}0.886{col 78}{space 4} .7975444{col 91}{space 3} 1.215874
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9241554{col 50}{space 2} .1945524{col 61}{space 1}   -0.37{col 70}{space 3}0.708{col 78}{space 4} .6116401{col 91}{space 3} 1.396349
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .8155661{col 50}{space 2} .1400316{col 61}{space 1}   -1.19{col 70}{space 3}0.235{col 78}{space 4} .5824577{col 91}{space 3} 1.141968
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .9556425{col 50}{space 2} .2910917{col 61}{space 1}   -0.15{col 70}{space 3}0.882{col 78}{space 4} .5259417{col 91}{space 3} 1.736414
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.167665{col 50}{space 2} .2125261{col 61}{space 1}    0.85{col 70}{space 3}0.394{col 78}{space 4} .8172294{col 91}{space 3}  1.66837
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.069154{col 50}{space 2}  .189471{col 61}{space 1}    0.38{col 70}{space 3}0.706{col 78}{space 4} .7553496{col 91}{space 3} 1.513327
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.117951{col 50}{space 2} .1984927{col 61}{space 1}    0.63{col 70}{space 3}0.530{col 78}{space 4} .7893058{col 91}{space 3} 1.583435
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.264681{col 50}{space 2}  .224145{col 61}{space 1}    1.32{col 70}{space 3}0.185{col 78}{space 4} .8934554{col 91}{space 3}  1.79015
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .1248602{col 50}{space 2} .0272465{col 61}{space 1}   -9.53{col 70}{space 3}0.000{col 78}{space 4} .0813994{col 91}{space 3} .1915256
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit tech_worsen i.isco08_5group $controls_demographics $controls_objective if infullmodel_TableD2 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      2.57
{txt}{col 49}Prob > F{col 67}= {res}    0.0001

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                         tech_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.145882{col 50}{space 2} .1645278{col 61}{space 1}    0.95{col 70}{space 3}0.343{col 78}{space 4} .8647401{col 91}{space 3} 1.518428
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.295141{col 50}{space 2} .2747104{col 61}{space 1}    1.22{col 70}{space 3}0.223{col 78}{space 4}  .854505{col 91}{space 3} 1.962995
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .9488207{col 50}{space 2} .1787633{col 61}{space 1}   -0.28{col 70}{space 3}0.780{col 78}{space 4} .6557886{col 91}{space 3} 1.372791
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.531672{col 50}{space 2} .3715764{col 61}{space 1}    1.76{col 70}{space 3}0.079{col 78}{space 4} .9519308{col 91}{space 3} 2.464486
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.316433{col 50}{space 2} .1963717{col 61}{space 1}    1.84{col 70}{space 3}0.065{col 78}{space 4}  .982622{col 91}{space 3} 1.763644
{txt}{space 32}50+  {c |}{col 38}{res}{space 2}  1.29173{col 50}{space 2} .2050114{col 61}{space 1}    1.61{col 70}{space 3}0.107{col 78}{space 4} .9463145{col 91}{space 3} 1.763225
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}  .868358{col 50}{space 2} .0862481{col 61}{space 1}   -1.42{col 70}{space 3}0.155{col 78}{space 4} .7147084{col 91}{space 3} 1.055039
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  1.10359{col 50}{space 2}  .116495{col 61}{space 1}    0.93{col 70}{space 3}0.350{col 78}{space 4} .8972786{col 91}{space 3} 1.357338
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.248167{col 50}{space 2} .1471494{col 61}{space 1}    1.88{col 70}{space 3}0.060{col 78}{space 4} .9905848{col 91}{space 3} 1.572728
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.037156{col 50}{space 2} .1137139{col 61}{space 1}    0.33{col 70}{space 3}0.739{col 78}{space 4} .8365462{col 91}{space 3} 1.285874
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9606719{col 50}{space 2} .2051961{col 61}{space 1}   -0.19{col 70}{space 3}0.851{col 78}{space 4} .6319838{col 91}{space 3} 1.460307
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .8244381{col 50}{space 2} .1417613{col 61}{space 1}   -1.12{col 70}{space 3}0.262{col 78}{space 4} .5885049{col 91}{space 3} 1.154958
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2}  .951978{col 50}{space 2} .2910022{col 61}{space 1}   -0.16{col 70}{space 3}0.872{col 78}{space 4} .5228182{col 91}{space 3} 1.733417
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.161317{col 50}{space 2} .2124477{col 61}{space 1}    0.82{col 70}{space 3}0.414{col 78}{space 4} .8113102{col 91}{space 3} 1.662319
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.074199{col 50}{space 2} .1910727{col 61}{space 1}    0.40{col 70}{space 3}0.687{col 78}{space 4} .7579338{col 91}{space 3} 1.522433
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.133929{col 50}{space 2} .2019321{col 61}{space 1}    0.71{col 70}{space 3}0.480{col 78}{space 4} .7997533{col 91}{space 3} 1.607739
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.255271{col 50}{space 2} .2217886{col 61}{space 1}    1.29{col 70}{space 3}0.198{col 78}{space 4} .8877618{col 91}{space 3}  1.77492
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .9881793{col 50}{space 2} .1211732{col 61}{space 1}   -0.10{col 70}{space 3}0.923{col 78}{space 4} .7770111{col 91}{space 3} 1.256737
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.529412{col 50}{space 2} .4135439{col 61}{space 1}    1.57{col 70}{space 3}0.116{col 78}{space 4} .9001072{col 91}{space 3} 2.598691
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.281864{col 50}{space 2} .3432878{col 61}{space 1}    0.93{col 70}{space 3}0.354{col 78}{space 4} .7582589{col 91}{space 3} 2.167038
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} 1.394314{col 50}{space 2} .3512092{col 61}{space 1}    1.32{col 70}{space 3}0.187{col 78}{space 4} .8509197{col 91}{space 3} 2.284717
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .8428629{col 50}{space 2} .1464735{col 61}{space 1}   -0.98{col 70}{space 3}0.325{col 78}{space 4} .5994999{col 91}{space 3} 1.185017
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.179514{col 50}{space 2} .1735202{col 61}{space 1}    1.12{col 70}{space 3}0.262{col 78}{space 4} .8839814{col 91}{space 3} 1.573849
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .0859055{col 50}{space 2} .0244577{col 61}{space 1}   -8.62{col 70}{space 3}0.000{col 78}{space 4} .0491593{col 91}{space 3}  .150119
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 2: Improve 
. svy: logit tech_improve i.isco08_5group if infullmodel_TableD2 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   4{txt},{res}   3959{txt}){col 67}= {res}     23.72
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                      tech_improve{col 36}{c |} Odds Ratio{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}isco08_5group {c |}
Technician/Associate Professional  {c |}{col 36}{res}{space 2} .7040158{col 48}{space 2} .0887544{col 59}{space 1}   -2.78{col 68}{space 3}0.005{col 76}{space 4} .5498443{col 89}{space 3} .9014158
{txt}{space 9}Clerical Support Workers  {c |}{col 36}{res}{space 2}  .342071{col 48}{space 2} .0526623{col 59}{space 1}   -6.97{col 68}{space 3}0.000{col 76}{space 4} .2529487{col 89}{space 3}  .462594
{txt}{space 8}Service and Sales Workers  {c |}{col 36}{res}{space 2} .3068644{col 48}{space 2} .0565368{col 59}{space 1}   -6.41{col 68}{space 3}0.000{col 76}{space 4} .2138322{col 89}{space 3} .4403723
{txt}{space 13}Manual Working Class  {c |}{col 36}{res}{space 2} .3564775{col 48}{space 2} .0659434{col 59}{space 1}   -5.58{col 68}{space 3}0.000{col 76}{space 4} .2480409{col 89}{space 3} .5123194
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .3114841{col 48}{space 2} .0200237{col 59}{space 1}  -18.14{col 68}{space 3}0.000{col 76}{space 4} .2745996{col 89}{space 3} .3533229
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit tech_improve i.isco08_5group $controls_demographics if infullmodel_TableD2 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}     10.37
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                        tech_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}  .783422{col 50}{space 2} .1021823{col 61}{space 1}   -1.87{col 70}{space 3}0.061{col 78}{space 4} .6066511{col 91}{space 3} 1.011702
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} .4522395{col 50}{space 2}  .074826{col 61}{space 1}   -4.80{col 70}{space 3}0.000{col 78}{space 4} .3269555{col 91}{space 3} .6255304
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .4080237{col 50}{space 2} .0803554{col 61}{space 1}   -4.55{col 70}{space 3}0.000{col 78}{space 4} .2773321{col 91}{space 3} .6003032
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .4044302{col 50}{space 2}  .082221{col 61}{space 1}   -4.45{col 70}{space 3}0.000{col 78}{space 4} .2714817{col 91}{space 3} .6024853
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .5985055{col 50}{space 2} .0790013{col 61}{space 1}   -3.89{col 70}{space 3}0.000{col 78}{space 4} .4620376{col 91}{space 3} .7752808
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .4215797{col 50}{space 2} .0637814{col 61}{space 1}   -5.71{col 70}{space 3}0.000{col 78}{space 4} .3133725{col 91}{space 3} .5671507
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6601585{col 50}{space 2} .0671241{col 61}{space 1}   -4.08{col 70}{space 3}0.000{col 78}{space 4} .5408448{col 91}{space 3} .8057937
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.265467{col 50}{space 2}  .133295{col 61}{space 1}    2.24{col 70}{space 3}0.025{col 78}{space 4} 1.029353{col 91}{space 3} 1.555742
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2}  .743296{col 50}{space 2} .1110058{col 61}{space 1}   -1.99{col 70}{space 3}0.047{col 78}{space 4} .5546286{col 91}{space 3} .9961423
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .7884021{col 50}{space 2} .0885944{col 61}{space 1}   -2.12{col 70}{space 3}0.034{col 78}{space 4} .6325097{col 91}{space 3} .9827167
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .7632024{col 50}{space 2} .1782472{col 61}{space 1}   -1.16{col 70}{space 3}0.247{col 78}{space 4} .4828136{col 91}{space 3} 1.206424
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9818739{col 50}{space 2}  .171683{col 61}{space 1}   -0.10{col 70}{space 3}0.917{col 78}{space 4} .6969083{col 91}{space 3} 1.383362
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8789707{col 50}{space 2} .3416891{col 61}{space 1}   -0.33{col 70}{space 3}0.740{col 78}{space 4} .4101844{col 91}{space 3} 1.883517
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5046275{col 50}{space 2} .1133756{col 61}{space 1}   -3.04{col 70}{space 3}0.002{col 78}{space 4} .3248415{col 91}{space 3} .7839176
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .5393284{col 50}{space 2} .1130688{col 61}{space 1}   -2.95{col 70}{space 3}0.003{col 78}{space 4}  .357558{col 91}{space 3} .8135046
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .6527976{col 50}{space 2} .1279229{col 61}{space 1}   -2.18{col 70}{space 3}0.030{col 78}{space 4} .4445547{col 91}{space 3} .9585877
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .8106742{col 50}{space 2} .1603535{col 61}{space 1}   -1.06{col 70}{space 3}0.289{col 78}{space 4} .5500789{col 91}{space 3} 1.194724
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .8529294{col 50}{space 2} .2005401{col 61}{space 1}   -0.68{col 70}{space 3}0.499{col 78}{space 4} .5379205{col 91}{space 3} 1.352409
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit tech_improve i.isco08_5group $controls_demographics $controls_objective if infullmodel_TableD2 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      8.76
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                        tech_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .8730948{col 50}{space 2} .1217765{col 61}{space 1}   -0.97{col 70}{space 3}0.331{col 78}{space 4} .6642051{col 91}{space 3} 1.147679
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} .6410654{col 50}{space 2} .1550765{col 61}{space 1}   -1.84{col 70}{space 3}0.066{col 78}{space 4} .3989607{col 91}{space 3} 1.030089
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .5559549{col 50}{space 2} .1282872{col 61}{space 1}   -2.54{col 70}{space 3}0.011{col 78}{space 4}  .353642{col 91}{space 3} .8740078
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .6599147{col 50}{space 2} .1820168{col 61}{space 1}   -1.51{col 70}{space 3}0.132{col 78}{space 4} .3842721{col 91}{space 3} 1.133279
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .6059595{col 50}{space 2} .0801221{col 61}{space 1}   -3.79{col 70}{space 3}0.000{col 78}{space 4} .4675847{col 91}{space 3} .7852841
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .4297908{col 50}{space 2} .0649978{col 61}{space 1}   -5.58{col 70}{space 3}0.000{col 78}{space 4} .3195138{col 91}{space 3}  .578129
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6844365{col 50}{space 2} .0702137{col 61}{space 1}   -3.70{col 70}{space 3}0.000{col 78}{space 4} .5597383{col 91}{space 3} .8369149
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.259845{col 50}{space 2}  .132008{col 61}{space 1}    2.20{col 70}{space 3}0.028{col 78}{space 4} 1.025888{col 91}{space 3} 1.547157
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .7404869{col 50}{space 2} .1100479{col 61}{space 1}   -2.02{col 70}{space 3}0.043{col 78}{space 4} .5533208{col 91}{space 3} .9909639
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .8021883{col 50}{space 2} .0926664{col 61}{space 1}   -1.91{col 70}{space 3}0.056{col 78}{space 4} .6396141{col 91}{space 3} 1.006085
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .7757171{col 50}{space 2} .1779182{col 61}{space 1}   -1.11{col 70}{space 3}0.268{col 78}{space 4} .4947803{col 91}{space 3}  1.21617
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.063545{col 50}{space 2} .1872545{col 61}{space 1}    0.35{col 70}{space 3}0.726{col 78}{space 4} .7530814{col 91}{space 3} 1.501998
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .9270945{col 50}{space 2} .3635164{col 61}{space 1}   -0.19{col 70}{space 3}0.847{col 78}{space 4} .4297971{col 91}{space 3} 1.999791
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5084147{col 50}{space 2} .1134948{col 61}{space 1}   -3.03{col 70}{space 3}0.002{col 78}{space 4} .3282041{col 91}{space 3} .7875756
{txt}{space 34}3  {c |}{col 38}{res}{space 2}  .526227{col 50}{space 2} .1103904{col 61}{space 1}   -3.06{col 70}{space 3}0.002{col 78}{space 4} .3487835{col 91}{space 3} .7939448
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .6499105{col 50}{space 2} .1271704{col 61}{space 1}   -2.20{col 70}{space 3}0.028{col 78}{space 4} .4428379{col 91}{space 3} .9538108
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .7725892{col 50}{space 2} .1535353{col 61}{space 1}   -1.30{col 70}{space 3}0.194{col 78}{space 4}  .523286{col 91}{space 3} 1.140665
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.274265{col 50}{space 2} .1638121{col 61}{space 1}    1.89{col 70}{space 3}0.059{col 78}{space 4} .9903772{col 91}{space 3} 1.639527
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.626424{col 50}{space 2}  .455619{col 61}{space 1}    1.74{col 70}{space 3}0.083{col 78}{space 4} .9390941{col 91}{space 3} 2.816815
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.166789{col 50}{space 2}  .320711{col 61}{space 1}    0.56{col 70}{space 3}0.575{col 78}{space 4} .6806977{col 91}{space 3} 2.000002
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .4380315{col 50}{space 2} .1188453{col 61}{space 1}   -3.04{col 70}{space 3}0.002{col 78}{space 4} .2573294{col 91}{space 3} .7456262
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .8297709{col 50}{space 2} .1214935{col 61}{space 1}   -1.27{col 70}{space 3}0.203{col 78}{space 4} .6227142{col 91}{space 3} 1.105675
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} .6009114{col 50}{space 2} .0793849{col 61}{space 1}   -3.86{col 70}{space 3}0.000{col 78}{space 4} .4637949{col 91}{space 3}  .778565
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .6160897{col 50}{space 2} .1882486{col 61}{space 1}   -1.59{col 70}{space 3}0.113{col 78}{space 4}  .338436{col 91}{space 3} 1.121531
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 3: Worsen v Improve
. svy: logit tech_directionworse i.isco08_5group if infullmodel_TableD2 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,324
{txt}{col 1}Number of PSUs{col 20}= {res}    1,324{txt}{col 49}Population size{col 67}={res} 1,363.3793
{txt}{col 49}Design df{col 67}= {res}     1,323
{txt}{col 49}F({res}   4{txt},{res}   1320{txt}){col 67}= {res}     16.11
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}               tech_directionworse{col 36}{c |} Odds Ratio{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}isco08_5group {c |}
Technician/Associate Professional  {c |}{col 36}{res}{space 2} 1.570847{col 48}{space 2} .2564931{col 59}{space 1}    2.77{col 68}{space 3}0.006{col 76}{space 4} 1.140301{col 89}{space 3} 2.163955
{txt}{space 9}Clerical Support Workers  {c |}{col 36}{res}{space 2} 3.806753{col 48}{space 2} .6884346{col 59}{space 1}    7.39{col 68}{space 3}0.000{col 76}{space 4} 2.669794{col 89}{space 3} 5.427899
{txt}{space 8}Service and Sales Workers  {c |}{col 36}{res}{space 2} 2.213998{col 48}{space 2} .5055853{col 59}{space 1}    3.48{col 68}{space 3}0.001{col 76}{space 4} 1.414556{col 89}{space 3} 3.465248
{txt}{space 13}Manual Working Class  {c |}{col 36}{res}{space 2} 2.518391{col 48}{space 2} .5774891{col 59}{space 1}    4.03{col 68}{space 3}0.000{col 76}{space 4}  1.60604{col 89}{space 3} 3.949025
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .6562588{col 48}{space 2}  .059319{col 59}{space 1}   -4.66{col 68}{space 3}0.000{col 76}{space 4} .5496229{col 89}{space 3} .7835838
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit tech_directionworse i.isco08_5group $controls_demographics if infullmodel_TableD2 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,324
{txt}{col 1}Number of PSUs{col 20}= {res}    1,324{txt}{col 49}Population size{col 67}={res} 1,363.3793
{txt}{col 49}Design df{col 67}= {res}     1,323
{txt}{col 49}F({res}  17{txt},{res}   1307{txt}){col 67}= {res}      5.64
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                 tech_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}  1.55857{col 50}{space 2} .2639934{col 61}{space 1}    2.62{col 70}{space 3}0.009{col 78}{space 4} 1.117934{col 91}{space 3} 2.172883
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 3.092465{col 50}{space 2} .6115659{col 61}{space 1}    5.71{col 70}{space 3}0.000{col 78}{space 4} 2.098056{col 91}{space 3} 4.558191
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 2.031276{col 50}{space 2} .5092101{col 61}{space 1}    2.83{col 70}{space 3}0.005{col 78}{space 4} 1.242196{col 91}{space 3} 3.321603
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 2.505983{col 50}{space 2} .6336438{col 61}{space 1}    3.63{col 70}{space 3}0.000{col 78}{space 4} 1.525993{col 91}{space 3} 4.115319
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.943099{col 50}{space 2} .3551903{col 61}{space 1}    3.63{col 70}{space 3}0.000{col 78}{space 4} 1.357552{col 91}{space 3} 2.781208
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 2.471226{col 50}{space 2} .4922539{col 61}{space 1}    4.54{col 70}{space 3}0.000{col 78}{space 4} 1.671871{col 91}{space 3} 3.652768
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} 1.217073{col 50}{space 2}  .161402{col 61}{space 1}    1.48{col 70}{space 3}0.139{col 78}{space 4} .9382779{col 91}{space 3} 1.578709
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .9196671{col 50}{space 2} .1255281{col 61}{space 1}   -0.61{col 70}{space 3}0.540{col 78}{space 4} .7036249{col 91}{space 3} 1.202043
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.559233{col 50}{space 2} .2768503{col 61}{space 1}    2.50{col 70}{space 3}0.012{col 78}{space 4} 1.100619{col 91}{space 3} 2.208945
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.171102{col 50}{space 2} .1735922{col 61}{space 1}    1.07{col 70}{space 3}0.287{col 78}{space 4} .8755998{col 91}{space 3} 1.566331
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.160133{col 50}{space 2} .3559632{col 61}{space 1}    0.48{col 70}{space 3}0.628{col 78}{space 4} .6354692{col 91}{space 3} 2.117975
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9135928{col 50}{space 2} .2204282{col 61}{space 1}   -0.37{col 70}{space 3}0.708{col 78}{space 4} .5691018{col 91}{space 3} 1.466613
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.085455{col 50}{space 2} .4890278{col 61}{space 1}    0.18{col 70}{space 3}0.856{col 78}{space 4} .4485086{col 91}{space 3} 2.626958
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.938953{col 50}{space 2} .5305978{col 61}{space 1}    2.42{col 70}{space 3}0.016{col 78}{space 4}   1.1335{col 91}{space 3} 3.316755
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.783753{col 50}{space 2} .4656758{col 61}{space 1}    2.22{col 70}{space 3}0.027{col 78}{space 4} 1.068839{col 91}{space 3} 2.976853
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.612297{col 50}{space 2} .3969295{col 61}{space 1}    1.94{col 70}{space 3}0.053{col 78}{space 4} .9947108{col 91}{space 3} 2.613324
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.469768{col 50}{space 2} .3620708{col 61}{space 1}    1.56{col 70}{space 3}0.118{col 78}{space 4} .9064985{col 91}{space 3} 2.383036
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .1993451{col 50}{space 2} .0601458{col 61}{space 1}   -5.35{col 70}{space 3}0.000{col 78}{space 4} .1102931{col 91}{space 3} .3602988
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit tech_directionworse i.isco08_5group $controls_demographics $controls_objective if infullmodel_TableD2 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,324
{txt}{col 1}Number of PSUs{col 20}= {res}    1,324{txt}{col 49}Population size{col 67}={res} 1,363.3793
{txt}{col 49}Design df{col 67}= {res}     1,323
{txt}{col 49}F({res}  23{txt},{res}   1301{txt}){col 67}= {res}      4.33
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                 tech_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.306476{col 50}{space 2} .2449688{col 61}{space 1}    1.43{col 70}{space 3}0.154{col 78}{space 4} .9043825{col 91}{space 3} 1.887343
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.758551{col 50}{space 2} .5149379{col 61}{space 1}    1.93{col 70}{space 3}0.054{col 78}{space 4} .9900981{col 91}{space 3} 3.123428
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.688526{col 50}{space 2} .4890938{col 61}{space 1}    1.81{col 70}{space 3}0.071{col 78}{space 4} .9565887{col 91}{space 3} 2.980508
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 2.096836{col 50}{space 2} .6928849{col 61}{space 1}    2.24{col 70}{space 3}0.025{col 78}{space 4} 1.096563{col 91}{space 3} 4.009551
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.938778{col 50}{space 2} .3521668{col 61}{space 1}    3.64{col 70}{space 3}0.000{col 78}{space 4} 1.357598{col 91}{space 3} 2.768758
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 2.417136{col 50}{space 2} .4802911{col 61}{space 1}    4.44{col 70}{space 3}0.000{col 78}{space 4} 1.636856{col 91}{space 3} 3.569373
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} 1.183359{col 50}{space 2} .1598184{col 61}{space 1}    1.25{col 70}{space 3}0.213{col 78}{space 4} .9079297{col 91}{space 3} 1.542342
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .9272337{col 50}{space 2} .1274089{col 61}{space 1}   -0.55{col 70}{space 3}0.583{col 78}{space 4} .7081424{col 91}{space 3} 1.214109
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.554239{col 50}{space 2} .2761429{col 61}{space 1}    2.48{col 70}{space 3}0.013{col 78}{space 4} 1.096846{col 91}{space 3} 2.202369
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.211282{col 50}{space 2} .1837786{col 61}{space 1}    1.26{col 70}{space 3}0.207{col 78}{space 4} .8994571{col 91}{space 3} 1.631209
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.204785{col 50}{space 2} .3707448{col 61}{space 1}    0.61{col 70}{space 3}0.545{col 78}{space 4} .6587672{col 91}{space 3} 2.203368
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .8523103{col 50}{space 2} .2068088{col 61}{space 1}   -0.66{col 70}{space 3}0.510{col 78}{space 4} .5295036{col 91}{space 3} 1.371913
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.041189{col 50}{space 2} .4682696{col 61}{space 1}    0.09{col 70}{space 3}0.929{col 78}{space 4} .4308787{col 91}{space 3}  2.51596
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.921505{col 50}{space 2} .5230416{col 61}{space 1}    2.40{col 70}{space 3}0.017{col 78}{space 4} 1.126494{col 91}{space 3} 3.277587
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.825607{col 50}{space 2} .4733348{col 61}{space 1}    2.32{col 70}{space 3}0.020{col 78}{space 4} 1.097766{col 91}{space 3} 3.036022
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.655714{col 50}{space 2} .4066966{col 61}{space 1}    2.05{col 70}{space 3}0.040{col 78}{space 4} 1.022613{col 91}{space 3} 2.680768
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.503107{col 50}{space 2} .3684373{col 61}{space 1}    1.66{col 70}{space 3}0.097{col 78}{space 4} .9292974{col 91}{space 3} 2.431224
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .8637467{col 50}{space 2} .1494429{col 61}{space 1}   -0.85{col 70}{space 3}0.397{col 78}{space 4} .6151474{col 91}{space 3} 1.212812
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} .8687686{col 50}{space 2} .3100844{col 61}{space 1}   -0.39{col 70}{space 3}0.694{col 78}{space 4}  .431332{col 91}{space 3} 1.749833
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} .9420662{col 50}{space 2} .3345992{col 61}{space 1}   -0.17{col 70}{space 3}0.867{col 78}{space 4} .4693299{col 91}{space 3}  1.89097
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2}  2.44301{col 50}{space 2} .8119485{col 61}{space 1}    2.69{col 70}{space 3}0.007{col 78}{space 4} 1.272812{col 91}{space 3} 4.689063
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.033409{col 50}{space 2} .2232271{col 61}{space 1}    0.15{col 70}{space 3}0.879{col 78}{space 4} .6764497{col 91}{space 3} 1.578734
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.559699{col 50}{space 2} .2770991{col 61}{space 1}    2.50{col 70}{space 3}0.012{col 78}{space 4} 1.100719{col 91}{space 3} 2.210067
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2127314{col 50}{space 2} .0828209{col 61}{space 1}   -3.98{col 70}{space 3}0.000{col 78}{space 4} .0991145{col 91}{space 3} .4565899
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. 
. ******* Employment Sector Group ********
. 
. * Model 1: Worsen 
. svy: logit tech_worsen i.employsector5 if infullmodel_TableD2 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   4{txt},{res}   3959{txt}){col 67}= {res}      0.48
{txt}{col 49}Prob > F{col 67}= {res}    0.7494

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                         tech_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9834627{col 50}{space 2} .1016107{col 61}{space 1}   -0.16{col 70}{space 3}0.872{col 78}{space 4} .8031293{col 91}{space 3} 1.204288
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9423062{col 50}{space 2} .1921377{col 61}{space 1}   -0.29{col 70}{space 3}0.771{col 78}{space 4} .6317972{col 91}{space 3} 1.405421
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2}  .807637{col 50}{space 2} .1302578{col 61}{space 1}   -1.32{col 70}{space 3}0.185{col 78}{space 4} .5886949{col 91}{space 3} 1.108006
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8795479{col 50}{space 2} .2620839{col 61}{space 1}   -0.43{col 70}{space 3}0.667{col 78}{space 4} .4903934{col 91}{space 3} 1.577518
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2129668{col 50}{space 2} .0142072{col 61}{space 1}  -23.18{col 70}{space 3}0.000{col 78}{space 4} .1868573{col 91}{space 3} .2427245
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit tech_worsen i.employsector5 $controls_demographics if infullmodel_TableD2 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      2.51
{txt}{col 49}Prob > F{col 67}= {res}    0.0006

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                         tech_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9847405{col 50}{space 2} .1058998{col 61}{space 1}   -0.14{col 70}{space 3}0.886{col 78}{space 4} .7975444{col 91}{space 3} 1.215874
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9241554{col 50}{space 2} .1945524{col 61}{space 1}   -0.37{col 70}{space 3}0.708{col 78}{space 4} .6116401{col 91}{space 3} 1.396349
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .8155661{col 50}{space 2} .1400316{col 61}{space 1}   -1.19{col 70}{space 3}0.235{col 78}{space 4} .5824577{col 91}{space 3} 1.141968
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .9556425{col 50}{space 2} .2910917{col 61}{space 1}   -0.15{col 70}{space 3}0.882{col 78}{space 4} .5259417{col 91}{space 3} 1.736414
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.288334{col 50}{space 2} .1934766{col 61}{space 1}    1.69{col 70}{space 3}0.092{col 78}{space 4} .9597529{col 91}{space 3} 1.729408
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.262694{col 50}{space 2} .2013064{col 61}{space 1}    1.46{col 70}{space 3}0.144{col 78}{space 4} .9237462{col 91}{space 3}  1.72601
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .8669435{col 50}{space 2} .0848512{col 61}{space 1}   -1.46{col 70}{space 3}0.145{col 78}{space 4} .7155746{col 91}{space 3} 1.050332
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.110653{col 50}{space 2} .1160975{col 61}{space 1}    1.00{col 70}{space 3}0.315{col 78}{space 4} .9048459{col 91}{space 3} 1.363272
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.259801{col 50}{space 2} .1461974{col 61}{space 1}    1.99{col 70}{space 3}0.047{col 78}{space 4}  1.00344{col 91}{space 3} 1.581657
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.167665{col 50}{space 2} .2125261{col 61}{space 1}    0.85{col 70}{space 3}0.394{col 78}{space 4} .8172294{col 91}{space 3}  1.66837
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.069154{col 50}{space 2}  .189471{col 61}{space 1}    0.38{col 70}{space 3}0.706{col 78}{space 4} .7553496{col 91}{space 3} 1.513327
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.117951{col 50}{space 2} .1984927{col 61}{space 1}    0.63{col 70}{space 3}0.530{col 78}{space 4} .7893058{col 91}{space 3} 1.583435
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.264681{col 50}{space 2}  .224145{col 61}{space 1}    1.32{col 70}{space 3}0.185{col 78}{space 4} .8934554{col 91}{space 3}  1.79015
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.282489{col 50}{space 2} .1698614{col 61}{space 1}    1.88{col 70}{space 3}0.060{col 78}{space 4} .9891918{col 91}{space 3}  1.66275
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.814161{col 50}{space 2} .2435089{col 61}{space 1}    4.44{col 70}{space 3}0.000{col 78}{space 4} 1.394398{col 91}{space 3} 2.360287
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .8573966{col 50}{space 2} .1444471{col 61}{space 1}   -0.91{col 70}{space 3}0.361{col 78}{space 4} .6162182{col 91}{space 3} 1.192968
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.127179{col 50}{space 2} .2059426{col 61}{space 1}    0.66{col 70}{space 3}0.512{col 78}{space 4} .7878174{col 91}{space 3} 1.612725
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .1248602{col 50}{space 2} .0272465{col 61}{space 1}   -9.53{col 70}{space 3}0.000{col 78}{space 4} .0813994{col 91}{space 3} .1915256
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit tech_worsen i.employsector5 $controls_demographics $controls_objective if infullmodel_TableD2 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      2.57
{txt}{col 49}Prob > F{col 67}= {res}    0.0001

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                         tech_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.037156{col 50}{space 2} .1137139{col 61}{space 1}    0.33{col 70}{space 3}0.739{col 78}{space 4} .8365462{col 91}{space 3} 1.285874
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9606719{col 50}{space 2} .2051961{col 61}{space 1}   -0.19{col 70}{space 3}0.851{col 78}{space 4} .6319838{col 91}{space 3} 1.460307
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .8244381{col 50}{space 2} .1417613{col 61}{space 1}   -1.12{col 70}{space 3}0.262{col 78}{space 4} .5885049{col 91}{space 3} 1.154958
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2}  .951978{col 50}{space 2} .2910022{col 61}{space 1}   -0.16{col 70}{space 3}0.872{col 78}{space 4} .5228182{col 91}{space 3} 1.733417
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.316433{col 50}{space 2} .1963717{col 61}{space 1}    1.84{col 70}{space 3}0.065{col 78}{space 4}  .982622{col 91}{space 3} 1.763644
{txt}{space 32}50+  {c |}{col 38}{res}{space 2}  1.29173{col 50}{space 2} .2050114{col 61}{space 1}    1.61{col 70}{space 3}0.107{col 78}{space 4} .9463145{col 91}{space 3} 1.763225
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}  .868358{col 50}{space 2} .0862481{col 61}{space 1}   -1.42{col 70}{space 3}0.155{col 78}{space 4} .7147084{col 91}{space 3} 1.055039
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  1.10359{col 50}{space 2}  .116495{col 61}{space 1}    0.93{col 70}{space 3}0.350{col 78}{space 4} .8972786{col 91}{space 3} 1.357338
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.248167{col 50}{space 2} .1471494{col 61}{space 1}    1.88{col 70}{space 3}0.060{col 78}{space 4} .9905848{col 91}{space 3} 1.572728
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.161317{col 50}{space 2} .2124477{col 61}{space 1}    0.82{col 70}{space 3}0.414{col 78}{space 4} .8113102{col 91}{space 3} 1.662319
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.074199{col 50}{space 2} .1910727{col 61}{space 1}    0.40{col 70}{space 3}0.687{col 78}{space 4} .7579338{col 91}{space 3} 1.522433
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.133929{col 50}{space 2} .2019321{col 61}{space 1}    0.71{col 70}{space 3}0.480{col 78}{space 4} .7997533{col 91}{space 3} 1.607739
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.255271{col 50}{space 2} .2217886{col 61}{space 1}    1.29{col 70}{space 3}0.198{col 78}{space 4} .8877618{col 91}{space 3}  1.77492
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.145882{col 50}{space 2} .1645278{col 61}{space 1}    0.95{col 70}{space 3}0.343{col 78}{space 4} .8647401{col 91}{space 3} 1.518428
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.295141{col 50}{space 2} .2747104{col 61}{space 1}    1.22{col 70}{space 3}0.223{col 78}{space 4}  .854505{col 91}{space 3} 1.962995
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .9488207{col 50}{space 2} .1787633{col 61}{space 1}   -0.28{col 70}{space 3}0.780{col 78}{space 4} .6557886{col 91}{space 3} 1.372791
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.531672{col 50}{space 2} .3715764{col 61}{space 1}    1.76{col 70}{space 3}0.079{col 78}{space 4} .9519308{col 91}{space 3} 2.464486
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .9881793{col 50}{space 2} .1211732{col 61}{space 1}   -0.10{col 70}{space 3}0.923{col 78}{space 4} .7770111{col 91}{space 3} 1.256737
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.529412{col 50}{space 2} .4135439{col 61}{space 1}    1.57{col 70}{space 3}0.116{col 78}{space 4} .9001072{col 91}{space 3} 2.598691
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.281864{col 50}{space 2} .3432878{col 61}{space 1}    0.93{col 70}{space 3}0.354{col 78}{space 4} .7582589{col 91}{space 3} 2.167038
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} 1.394314{col 50}{space 2} .3512092{col 61}{space 1}    1.32{col 70}{space 3}0.187{col 78}{space 4} .8509197{col 91}{space 3} 2.284717
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .8428629{col 50}{space 2} .1464735{col 61}{space 1}   -0.98{col 70}{space 3}0.325{col 78}{space 4} .5994999{col 91}{space 3} 1.185017
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.179514{col 50}{space 2} .1735202{col 61}{space 1}    1.12{col 70}{space 3}0.262{col 78}{space 4} .8839814{col 91}{space 3} 1.573849
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .0859055{col 50}{space 2} .0244577{col 61}{space 1}   -8.62{col 70}{space 3}0.000{col 78}{space 4} .0491593{col 91}{space 3}  .150119
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 2: Improve 
. svy: logit tech_improve i.employsector5 if infullmodel_TableD2 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   4{txt},{res}   3959{txt}){col 67}= {res}      2.01
{txt}{col 49}Prob > F{col 67}= {res}    0.0901

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                        tech_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .7829411{col 50}{space 2} .0829395{col 61}{space 1}   -2.31{col 70}{space 3}0.021{col 78}{space 4} .6361082{col 91}{space 3} .9636674
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .7117386{col 50}{space 2} .1537693{col 61}{space 1}   -1.57{col 70}{space 3}0.116{col 78}{space 4} .4659769{col 91}{space 3} 1.087118
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .8059765{col 50}{space 2} .1324907{col 61}{space 1}   -1.31{col 70}{space 3}0.190{col 78}{space 4} .5839217{col 91}{space 3} 1.112475
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .6260571{col 50}{space 2} .2337526{col 61}{space 1}   -1.25{col 70}{space 3}0.210{col 78}{space 4} .3010935{col 91}{space 3} 1.301747
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2}  .220707{col 50}{space 2} .0143948{col 61}{space 1}  -23.17{col 70}{space 3}0.000{col 78}{space 4} .1942149{col 91}{space 3} .2508127
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit tech_improve i.employsector5 $controls_demographics if infullmodel_TableD2 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}     10.37
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                        tech_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .7884021{col 50}{space 2} .0885944{col 61}{space 1}   -2.12{col 70}{space 3}0.034{col 78}{space 4} .6325097{col 91}{space 3} .9827167
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .7632024{col 50}{space 2} .1782472{col 61}{space 1}   -1.16{col 70}{space 3}0.247{col 78}{space 4} .4828136{col 91}{space 3} 1.206424
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9818739{col 50}{space 2}  .171683{col 61}{space 1}   -0.10{col 70}{space 3}0.917{col 78}{space 4} .6969083{col 91}{space 3} 1.383362
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8789707{col 50}{space 2} .3416891{col 61}{space 1}   -0.33{col 70}{space 3}0.740{col 78}{space 4} .4101844{col 91}{space 3} 1.883517
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .5985055{col 50}{space 2} .0790013{col 61}{space 1}   -3.89{col 70}{space 3}0.000{col 78}{space 4} .4620376{col 91}{space 3} .7752808
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .4215797{col 50}{space 2} .0637814{col 61}{space 1}   -5.71{col 70}{space 3}0.000{col 78}{space 4} .3133725{col 91}{space 3} .5671507
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6601585{col 50}{space 2} .0671241{col 61}{space 1}   -4.08{col 70}{space 3}0.000{col 78}{space 4} .5408448{col 91}{space 3} .8057937
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.265467{col 50}{space 2}  .133295{col 61}{space 1}    2.24{col 70}{space 3}0.025{col 78}{space 4} 1.029353{col 91}{space 3} 1.555742
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2}  .743296{col 50}{space 2} .1110058{col 61}{space 1}   -1.99{col 70}{space 3}0.047{col 78}{space 4} .5546286{col 91}{space 3} .9961423
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5046275{col 50}{space 2} .1133756{col 61}{space 1}   -3.04{col 70}{space 3}0.002{col 78}{space 4} .3248415{col 91}{space 3} .7839176
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .5393284{col 50}{space 2} .1130688{col 61}{space 1}   -2.95{col 70}{space 3}0.003{col 78}{space 4}  .357558{col 91}{space 3} .8135046
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .6527976{col 50}{space 2} .1279229{col 61}{space 1}   -2.18{col 70}{space 3}0.030{col 78}{space 4} .4445547{col 91}{space 3} .9585877
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .8106742{col 50}{space 2} .1603535{col 61}{space 1}   -1.06{col 70}{space 3}0.289{col 78}{space 4} .5500789{col 91}{space 3} 1.194724
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}  .783422{col 50}{space 2} .1021823{col 61}{space 1}   -1.87{col 70}{space 3}0.061{col 78}{space 4} .6066511{col 91}{space 3} 1.011702
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} .4522395{col 50}{space 2}  .074826{col 61}{space 1}   -4.80{col 70}{space 3}0.000{col 78}{space 4} .3269555{col 91}{space 3} .6255304
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .4080237{col 50}{space 2} .0803554{col 61}{space 1}   -4.55{col 70}{space 3}0.000{col 78}{space 4} .2773321{col 91}{space 3} .6003032
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .4044302{col 50}{space 2}  .082221{col 61}{space 1}   -4.45{col 70}{space 3}0.000{col 78}{space 4} .2714817{col 91}{space 3} .6024853
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .8529294{col 50}{space 2} .2005401{col 61}{space 1}   -0.68{col 70}{space 3}0.499{col 78}{space 4} .5379205{col 91}{space 3} 1.352409
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit tech_improve i.employsector5 $controls_demographics $controls_objective if infullmodel_TableD2 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      8.76
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                        tech_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .8021883{col 50}{space 2} .0926664{col 61}{space 1}   -1.91{col 70}{space 3}0.056{col 78}{space 4} .6396141{col 91}{space 3} 1.006085
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .7757171{col 50}{space 2} .1779182{col 61}{space 1}   -1.11{col 70}{space 3}0.268{col 78}{space 4} .4947803{col 91}{space 3}  1.21617
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.063545{col 50}{space 2} .1872545{col 61}{space 1}    0.35{col 70}{space 3}0.726{col 78}{space 4} .7530814{col 91}{space 3} 1.501998
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .9270945{col 50}{space 2} .3635164{col 61}{space 1}   -0.19{col 70}{space 3}0.847{col 78}{space 4} .4297971{col 91}{space 3} 1.999791
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .6059595{col 50}{space 2} .0801221{col 61}{space 1}   -3.79{col 70}{space 3}0.000{col 78}{space 4} .4675847{col 91}{space 3} .7852841
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .4297908{col 50}{space 2} .0649978{col 61}{space 1}   -5.58{col 70}{space 3}0.000{col 78}{space 4} .3195138{col 91}{space 3}  .578129
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6844365{col 50}{space 2} .0702137{col 61}{space 1}   -3.70{col 70}{space 3}0.000{col 78}{space 4} .5597383{col 91}{space 3} .8369149
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.259845{col 50}{space 2}  .132008{col 61}{space 1}    2.20{col 70}{space 3}0.028{col 78}{space 4} 1.025888{col 91}{space 3} 1.547157
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .7404869{col 50}{space 2} .1100479{col 61}{space 1}   -2.02{col 70}{space 3}0.043{col 78}{space 4} .5533208{col 91}{space 3} .9909639
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5084147{col 50}{space 2} .1134948{col 61}{space 1}   -3.03{col 70}{space 3}0.002{col 78}{space 4} .3282041{col 91}{space 3} .7875756
{txt}{space 34}3  {c |}{col 38}{res}{space 2}  .526227{col 50}{space 2} .1103904{col 61}{space 1}   -3.06{col 70}{space 3}0.002{col 78}{space 4} .3487835{col 91}{space 3} .7939448
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .6499105{col 50}{space 2} .1271704{col 61}{space 1}   -2.20{col 70}{space 3}0.028{col 78}{space 4} .4428379{col 91}{space 3} .9538108
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .7725892{col 50}{space 2} .1535353{col 61}{space 1}   -1.30{col 70}{space 3}0.194{col 78}{space 4}  .523286{col 91}{space 3} 1.140665
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .8730948{col 50}{space 2} .1217765{col 61}{space 1}   -0.97{col 70}{space 3}0.331{col 78}{space 4} .6642051{col 91}{space 3} 1.147679
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} .6410654{col 50}{space 2} .1550765{col 61}{space 1}   -1.84{col 70}{space 3}0.066{col 78}{space 4} .3989607{col 91}{space 3} 1.030089
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .5559549{col 50}{space 2} .1282872{col 61}{space 1}   -2.54{col 70}{space 3}0.011{col 78}{space 4}  .353642{col 91}{space 3} .8740078
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .6599147{col 50}{space 2} .1820168{col 61}{space 1}   -1.51{col 70}{space 3}0.132{col 78}{space 4} .3842721{col 91}{space 3} 1.133279
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.274265{col 50}{space 2} .1638121{col 61}{space 1}    1.89{col 70}{space 3}0.059{col 78}{space 4} .9903772{col 91}{space 3} 1.639527
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.626424{col 50}{space 2}  .455619{col 61}{space 1}    1.74{col 70}{space 3}0.083{col 78}{space 4} .9390941{col 91}{space 3} 2.816815
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.166789{col 50}{space 2}  .320711{col 61}{space 1}    0.56{col 70}{space 3}0.575{col 78}{space 4} .6806977{col 91}{space 3} 2.000002
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .4380315{col 50}{space 2} .1188453{col 61}{space 1}   -3.04{col 70}{space 3}0.002{col 78}{space 4} .2573294{col 91}{space 3} .7456262
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .8297709{col 50}{space 2} .1214935{col 61}{space 1}   -1.27{col 70}{space 3}0.203{col 78}{space 4} .6227142{col 91}{space 3} 1.105675
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} .6009114{col 50}{space 2} .0793849{col 61}{space 1}   -3.86{col 70}{space 3}0.000{col 78}{space 4} .4637949{col 91}{space 3}  .778565
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .6160897{col 50}{space 2} .1882486{col 61}{space 1}   -1.59{col 70}{space 3}0.113{col 78}{space 4}  .338436{col 91}{space 3} 1.121531
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 3: Worsen v Improve
. svy: logit tech_directionworse i.employsector5 if infullmodel_TableD2 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,324
{txt}{col 1}Number of PSUs{col 20}= {res}    1,324{txt}{col 49}Population size{col 67}={res} 1,363.3793
{txt}{col 49}Design df{col 67}= {res}     1,323
{txt}{col 49}F({res}   4{txt},{res}   1320{txt}){col 67}= {res}      0.69
{txt}{col 49}Prob > F{col 67}= {res}    0.5986

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                 tech_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.210332{col 50}{space 2} .1637425{col 61}{space 1}    1.41{col 70}{space 3}0.158{col 78}{space 4} .9282014{col 91}{space 3} 1.578217
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}  1.26779{col 50}{space 2}  .346969{col 61}{space 1}    0.87{col 70}{space 3}0.386{col 78}{space 4} .7411002{col 91}{space 3}  2.16879
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.000706{col 50}{space 2} .2107763{col 61}{space 1}    0.00{col 70}{space 3}0.997{col 78}{space 4} .6619967{col 91}{space 3} 1.512715
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.338216{col 50}{space 2} .5926274{col 61}{space 1}    0.66{col 70}{space 3}0.511{col 78}{space 4} .5613422{col 91}{space 3} 3.190252
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .9710874{col 50}{space 2} .0820991{col 61}{space 1}   -0.35{col 70}{space 3}0.729{col 78}{space 4} .8226761{col 91}{space 3} 1.146272
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit tech_directionworse i.employsector5 $controls_demographics if infullmodel_TableD2 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,324
{txt}{col 1}Number of PSUs{col 20}= {res}    1,324{txt}{col 49}Population size{col 67}={res} 1,363.3793
{txt}{col 49}Design df{col 67}= {res}     1,323
{txt}{col 49}F({res}  17{txt},{res}   1307{txt}){col 67}= {res}      5.64
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                 tech_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.171102{col 50}{space 2} .1735922{col 61}{space 1}    1.07{col 70}{space 3}0.287{col 78}{space 4} .8755998{col 91}{space 3} 1.566331
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.160133{col 50}{space 2} .3559632{col 61}{space 1}    0.48{col 70}{space 3}0.628{col 78}{space 4} .6354692{col 91}{space 3} 2.117975
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9135928{col 50}{space 2} .2204282{col 61}{space 1}   -0.37{col 70}{space 3}0.708{col 78}{space 4} .5691018{col 91}{space 3} 1.466613
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.085455{col 50}{space 2} .4890278{col 61}{space 1}    0.18{col 70}{space 3}0.856{col 78}{space 4} .4485086{col 91}{space 3} 2.626958
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.943099{col 50}{space 2} .3551903{col 61}{space 1}    3.63{col 70}{space 3}0.000{col 78}{space 4} 1.357552{col 91}{space 3} 2.781208
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 2.471226{col 50}{space 2} .4922539{col 61}{space 1}    4.54{col 70}{space 3}0.000{col 78}{space 4} 1.671871{col 91}{space 3} 3.652768
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} 1.217073{col 50}{space 2}  .161402{col 61}{space 1}    1.48{col 70}{space 3}0.139{col 78}{space 4} .9382779{col 91}{space 3} 1.578709
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .9196671{col 50}{space 2} .1255281{col 61}{space 1}   -0.61{col 70}{space 3}0.540{col 78}{space 4} .7036249{col 91}{space 3} 1.202043
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.559233{col 50}{space 2} .2768503{col 61}{space 1}    2.50{col 70}{space 3}0.012{col 78}{space 4} 1.100619{col 91}{space 3} 2.208945
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.938953{col 50}{space 2} .5305978{col 61}{space 1}    2.42{col 70}{space 3}0.016{col 78}{space 4}   1.1335{col 91}{space 3} 3.316755
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.783753{col 50}{space 2} .4656758{col 61}{space 1}    2.22{col 70}{space 3}0.027{col 78}{space 4} 1.068839{col 91}{space 3} 2.976853
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.612297{col 50}{space 2} .3969295{col 61}{space 1}    1.94{col 70}{space 3}0.053{col 78}{space 4} .9947108{col 91}{space 3} 2.613324
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.469768{col 50}{space 2} .3620708{col 61}{space 1}    1.56{col 70}{space 3}0.118{col 78}{space 4} .9064985{col 91}{space 3} 2.383036
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}  1.55857{col 50}{space 2} .2639934{col 61}{space 1}    2.62{col 70}{space 3}0.009{col 78}{space 4} 1.117934{col 91}{space 3} 2.172883
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 3.092465{col 50}{space 2} .6115659{col 61}{space 1}    5.71{col 70}{space 3}0.000{col 78}{space 4} 2.098056{col 91}{space 3} 4.558191
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 2.031276{col 50}{space 2} .5092101{col 61}{space 1}    2.83{col 70}{space 3}0.005{col 78}{space 4} 1.242196{col 91}{space 3} 3.321603
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 2.505983{col 50}{space 2} .6336438{col 61}{space 1}    3.63{col 70}{space 3}0.000{col 78}{space 4} 1.525993{col 91}{space 3} 4.115319
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .1993451{col 50}{space 2} .0601458{col 61}{space 1}   -5.35{col 70}{space 3}0.000{col 78}{space 4} .1102931{col 91}{space 3} .3602988
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit tech_directionworse i.employsector5 $controls_demographics $controls_objective if infullmodel_TableD2 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,324
{txt}{col 1}Number of PSUs{col 20}= {res}    1,324{txt}{col 49}Population size{col 67}={res} 1,363.3793
{txt}{col 49}Design df{col 67}= {res}     1,323
{txt}{col 49}F({res}  23{txt},{res}   1301{txt}){col 67}= {res}      4.33
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                 tech_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.211282{col 50}{space 2} .1837786{col 61}{space 1}    1.26{col 70}{space 3}0.207{col 78}{space 4} .8994571{col 91}{space 3} 1.631209
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.204785{col 50}{space 2} .3707448{col 61}{space 1}    0.61{col 70}{space 3}0.545{col 78}{space 4} .6587672{col 91}{space 3} 2.203368
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .8523103{col 50}{space 2} .2068088{col 61}{space 1}   -0.66{col 70}{space 3}0.510{col 78}{space 4} .5295036{col 91}{space 3} 1.371913
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.041189{col 50}{space 2} .4682696{col 61}{space 1}    0.09{col 70}{space 3}0.929{col 78}{space 4} .4308787{col 91}{space 3}  2.51596
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.938778{col 50}{space 2} .3521668{col 61}{space 1}    3.64{col 70}{space 3}0.000{col 78}{space 4} 1.357598{col 91}{space 3} 2.768758
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 2.417136{col 50}{space 2} .4802911{col 61}{space 1}    4.44{col 70}{space 3}0.000{col 78}{space 4} 1.636856{col 91}{space 3} 3.569373
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} 1.183359{col 50}{space 2} .1598184{col 61}{space 1}    1.25{col 70}{space 3}0.213{col 78}{space 4} .9079297{col 91}{space 3} 1.542342
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .9272337{col 50}{space 2} .1274089{col 61}{space 1}   -0.55{col 70}{space 3}0.583{col 78}{space 4} .7081424{col 91}{space 3} 1.214109
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.554239{col 50}{space 2} .2761429{col 61}{space 1}    2.48{col 70}{space 3}0.013{col 78}{space 4} 1.096846{col 91}{space 3} 2.202369
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.921505{col 50}{space 2} .5230416{col 61}{space 1}    2.40{col 70}{space 3}0.017{col 78}{space 4} 1.126494{col 91}{space 3} 3.277587
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.825607{col 50}{space 2} .4733348{col 61}{space 1}    2.32{col 70}{space 3}0.020{col 78}{space 4} 1.097766{col 91}{space 3} 3.036022
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.655714{col 50}{space 2} .4066966{col 61}{space 1}    2.05{col 70}{space 3}0.040{col 78}{space 4} 1.022613{col 91}{space 3} 2.680768
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.503107{col 50}{space 2} .3684373{col 61}{space 1}    1.66{col 70}{space 3}0.097{col 78}{space 4} .9292974{col 91}{space 3} 2.431224
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.306476{col 50}{space 2} .2449688{col 61}{space 1}    1.43{col 70}{space 3}0.154{col 78}{space 4} .9043825{col 91}{space 3} 1.887343
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.758551{col 50}{space 2} .5149379{col 61}{space 1}    1.93{col 70}{space 3}0.054{col 78}{space 4} .9900981{col 91}{space 3} 3.123428
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.688526{col 50}{space 2} .4890938{col 61}{space 1}    1.81{col 70}{space 3}0.071{col 78}{space 4} .9565887{col 91}{space 3} 2.980508
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 2.096836{col 50}{space 2} .6928849{col 61}{space 1}    2.24{col 70}{space 3}0.025{col 78}{space 4} 1.096563{col 91}{space 3} 4.009551
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .8637467{col 50}{space 2} .1494429{col 61}{space 1}   -0.85{col 70}{space 3}0.397{col 78}{space 4} .6151474{col 91}{space 3} 1.212812
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} .8687686{col 50}{space 2} .3100844{col 61}{space 1}   -0.39{col 70}{space 3}0.694{col 78}{space 4}  .431332{col 91}{space 3} 1.749833
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} .9420662{col 50}{space 2} .3345992{col 61}{space 1}   -0.17{col 70}{space 3}0.867{col 78}{space 4} .4693299{col 91}{space 3}  1.89097
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2}  2.44301{col 50}{space 2} .8119485{col 61}{space 1}    2.69{col 70}{space 3}0.007{col 78}{space 4} 1.272812{col 91}{space 3} 4.689063
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.033409{col 50}{space 2} .2232271{col 61}{space 1}    0.15{col 70}{space 3}0.879{col 78}{space 4} .6764497{col 91}{space 3} 1.578734
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.559699{col 50}{space 2} .2770991{col 61}{space 1}    2.50{col 70}{space 3}0.012{col 78}{space 4} 1.100719{col 91}{space 3} 2.210067
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2127314{col 50}{space 2} .0828209{col 61}{space 1}   -3.98{col 70}{space 3}0.000{col 78}{space 4} .0991145{col 91}{space 3} .4565899
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. 
. ******* Equivalised HH Income ********
. 
. 
. * Model 1: Worsen 
. svy: logit tech_worsen i.equivincomeHHquintile_HBAI_imp if infullmodel_TableD2 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   4{txt},{res}   3959{txt}){col 67}= {res}      0.47
{txt}{col 49}Prob > F{col 67}= {res}    0.7580

{txt}{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 32}{c |}{col 44}  Linearized
{col 1}                   tech_worsen{col 32}{c |} Odds Ratio{col 44}   Std. Err.{col 56}      t{col 64}   P>|t|{col 72}     [95% Con{col 85}f. Interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
equivincomeHHquintile_HBAI_imp {c |}
{space 28}2  {c |}{col 32}{res}{space 2}   1.2166{col 44}{space 2} .2189865{col 55}{space 1}    1.09{col 64}{space 3}0.276{col 72}{space 4}  .854842{col 85}{space 3}  1.73145
{txt}{space 28}3  {c |}{col 32}{res}{space 2} 1.100139{col 44}{space 2}  .190454{col 55}{space 1}    0.55{col 64}{space 3}0.581{col 72}{space 4} .7835096{col 85}{space 3} 1.544725
{txt}{space 28}4  {c |}{col 32}{res}{space 2} 1.129761{col 44}{space 2} .1892023{col 55}{space 1}    0.73{col 64}{space 3}0.466{col 72}{space 4} .8135636{col 85}{space 3} 1.568851
{txt}{space 17}Top Quintile  {c |}{col 32}{res}{space 2} 1.211157{col 44}{space 2} .1929776{col 55}{space 1}    1.20{col 64}{space 3}0.229{col 72}{space 4} .8862048{col 85}{space 3} 1.655261
{txt}{space 30} {c |}
{space 25}_cons {c |}{col 32}{res}{space 2} .1800037{col 44}{space 2} .0246062{col 55}{space 1}  -12.54{col 64}{space 3}0.000{col 72}{space 4} .1376854{col 85}{space 3} .2353286
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit tech_worsen i.equivincomeHHquintile_HBAI_imp $controls_demographics if infullmodel_TableD2 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      2.51
{txt}{col 49}Prob > F{col 67}= {res}    0.0006

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                         tech_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.167665{col 50}{space 2} .2125261{col 61}{space 1}    0.85{col 70}{space 3}0.394{col 78}{space 4} .8172294{col 91}{space 3}  1.66837
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.069154{col 50}{space 2}  .189471{col 61}{space 1}    0.38{col 70}{space 3}0.706{col 78}{space 4} .7553496{col 91}{space 3} 1.513327
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.117951{col 50}{space 2} .1984927{col 61}{space 1}    0.63{col 70}{space 3}0.530{col 78}{space 4} .7893058{col 91}{space 3} 1.583435
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.264681{col 50}{space 2}  .224145{col 61}{space 1}    1.32{col 70}{space 3}0.185{col 78}{space 4} .8934554{col 91}{space 3}  1.79015
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.288334{col 50}{space 2} .1934766{col 61}{space 1}    1.69{col 70}{space 3}0.092{col 78}{space 4} .9597529{col 91}{space 3} 1.729408
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.262694{col 50}{space 2} .2013064{col 61}{space 1}    1.46{col 70}{space 3}0.144{col 78}{space 4} .9237462{col 91}{space 3}  1.72601
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .8669435{col 50}{space 2} .0848512{col 61}{space 1}   -1.46{col 70}{space 3}0.145{col 78}{space 4} .7155746{col 91}{space 3} 1.050332
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.110653{col 50}{space 2} .1160975{col 61}{space 1}    1.00{col 70}{space 3}0.315{col 78}{space 4} .9048459{col 91}{space 3} 1.363272
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.259801{col 50}{space 2} .1461974{col 61}{space 1}    1.99{col 70}{space 3}0.047{col 78}{space 4}  1.00344{col 91}{space 3} 1.581657
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9847405{col 50}{space 2} .1058998{col 61}{space 1}   -0.14{col 70}{space 3}0.886{col 78}{space 4} .7975444{col 91}{space 3} 1.215874
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9241554{col 50}{space 2} .1945524{col 61}{space 1}   -0.37{col 70}{space 3}0.708{col 78}{space 4} .6116401{col 91}{space 3} 1.396349
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .8155661{col 50}{space 2} .1400316{col 61}{space 1}   -1.19{col 70}{space 3}0.235{col 78}{space 4} .5824577{col 91}{space 3} 1.141968
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .9556425{col 50}{space 2} .2910917{col 61}{space 1}   -0.15{col 70}{space 3}0.882{col 78}{space 4} .5259417{col 91}{space 3} 1.736414
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.282489{col 50}{space 2} .1698614{col 61}{space 1}    1.88{col 70}{space 3}0.060{col 78}{space 4} .9891918{col 91}{space 3}  1.66275
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.814161{col 50}{space 2} .2435089{col 61}{space 1}    4.44{col 70}{space 3}0.000{col 78}{space 4} 1.394398{col 91}{space 3} 2.360287
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .8573966{col 50}{space 2} .1444471{col 61}{space 1}   -0.91{col 70}{space 3}0.361{col 78}{space 4} .6162182{col 91}{space 3} 1.192968
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.127179{col 50}{space 2} .2059426{col 61}{space 1}    0.66{col 70}{space 3}0.512{col 78}{space 4} .7878174{col 91}{space 3} 1.612725
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .1248602{col 50}{space 2} .0272465{col 61}{space 1}   -9.53{col 70}{space 3}0.000{col 78}{space 4} .0813994{col 91}{space 3} .1915256
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit tech_worsen i.equivincomeHHquintile_HBAI_imp $controls_demographics $controls_objective if infullmodel_TableD2 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      2.57
{txt}{col 49}Prob > F{col 67}= {res}    0.0001

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                         tech_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.161317{col 50}{space 2} .2124477{col 61}{space 1}    0.82{col 70}{space 3}0.414{col 78}{space 4} .8113102{col 91}{space 3} 1.662319
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.074199{col 50}{space 2} .1910727{col 61}{space 1}    0.40{col 70}{space 3}0.687{col 78}{space 4} .7579338{col 91}{space 3} 1.522433
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.133929{col 50}{space 2} .2019321{col 61}{space 1}    0.71{col 70}{space 3}0.480{col 78}{space 4} .7997533{col 91}{space 3} 1.607739
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.255271{col 50}{space 2} .2217886{col 61}{space 1}    1.29{col 70}{space 3}0.198{col 78}{space 4} .8877618{col 91}{space 3}  1.77492
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.316433{col 50}{space 2} .1963717{col 61}{space 1}    1.84{col 70}{space 3}0.065{col 78}{space 4}  .982622{col 91}{space 3} 1.763644
{txt}{space 32}50+  {c |}{col 38}{res}{space 2}  1.29173{col 50}{space 2} .2050114{col 61}{space 1}    1.61{col 70}{space 3}0.107{col 78}{space 4} .9463145{col 91}{space 3} 1.763225
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}  .868358{col 50}{space 2} .0862481{col 61}{space 1}   -1.42{col 70}{space 3}0.155{col 78}{space 4} .7147084{col 91}{space 3} 1.055039
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  1.10359{col 50}{space 2}  .116495{col 61}{space 1}    0.93{col 70}{space 3}0.350{col 78}{space 4} .8972786{col 91}{space 3} 1.357338
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.248167{col 50}{space 2} .1471494{col 61}{space 1}    1.88{col 70}{space 3}0.060{col 78}{space 4} .9905848{col 91}{space 3} 1.572728
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.037156{col 50}{space 2} .1137139{col 61}{space 1}    0.33{col 70}{space 3}0.739{col 78}{space 4} .8365462{col 91}{space 3} 1.285874
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9606719{col 50}{space 2} .2051961{col 61}{space 1}   -0.19{col 70}{space 3}0.851{col 78}{space 4} .6319838{col 91}{space 3} 1.460307
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .8244381{col 50}{space 2} .1417613{col 61}{space 1}   -1.12{col 70}{space 3}0.262{col 78}{space 4} .5885049{col 91}{space 3} 1.154958
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2}  .951978{col 50}{space 2} .2910022{col 61}{space 1}   -0.16{col 70}{space 3}0.872{col 78}{space 4} .5228182{col 91}{space 3} 1.733417
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.145882{col 50}{space 2} .1645278{col 61}{space 1}    0.95{col 70}{space 3}0.343{col 78}{space 4} .8647401{col 91}{space 3} 1.518428
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.295141{col 50}{space 2} .2747104{col 61}{space 1}    1.22{col 70}{space 3}0.223{col 78}{space 4}  .854505{col 91}{space 3} 1.962995
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .9488207{col 50}{space 2} .1787633{col 61}{space 1}   -0.28{col 70}{space 3}0.780{col 78}{space 4} .6557886{col 91}{space 3} 1.372791
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.531672{col 50}{space 2} .3715764{col 61}{space 1}    1.76{col 70}{space 3}0.079{col 78}{space 4} .9519308{col 91}{space 3} 2.464486
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .9881793{col 50}{space 2} .1211732{col 61}{space 1}   -0.10{col 70}{space 3}0.923{col 78}{space 4} .7770111{col 91}{space 3} 1.256737
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.529412{col 50}{space 2} .4135439{col 61}{space 1}    1.57{col 70}{space 3}0.116{col 78}{space 4} .9001072{col 91}{space 3} 2.598691
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.281864{col 50}{space 2} .3432878{col 61}{space 1}    0.93{col 70}{space 3}0.354{col 78}{space 4} .7582589{col 91}{space 3} 2.167038
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} 1.394314{col 50}{space 2} .3512092{col 61}{space 1}    1.32{col 70}{space 3}0.187{col 78}{space 4} .8509197{col 91}{space 3} 2.284717
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .8428629{col 50}{space 2} .1464735{col 61}{space 1}   -0.98{col 70}{space 3}0.325{col 78}{space 4} .5994999{col 91}{space 3} 1.185017
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.179514{col 50}{space 2} .1735202{col 61}{space 1}    1.12{col 70}{space 3}0.262{col 78}{space 4} .8839814{col 91}{space 3} 1.573849
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .0859055{col 50}{space 2} .0244577{col 61}{space 1}   -8.62{col 70}{space 3}0.000{col 78}{space 4} .0491593{col 91}{space 3}  .150119
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 2: Improve 
. svy: logit tech_improve i.equivincomeHHquintile_HBAI_imp if infullmodel_TableD2 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   4{txt},{res}   3959{txt}){col 67}= {res}     15.69
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 32}{c |}{col 44}  Linearized
{col 1}                  tech_improve{col 32}{c |} Odds Ratio{col 44}   Std. Err.{col 56}      t{col 64}   P>|t|{col 72}     [95% Con{col 85}f. Interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
equivincomeHHquintile_HBAI_imp {c |}
{space 28}2  {c |}{col 32}{res}{space 2} .5392734{col 44}{space 2} .1167225{col 55}{space 1}   -2.85{col 64}{space 3}0.004{col 72}{space 4} .3527891{col 85}{space 3} .8243333
{txt}{space 28}3  {c |}{col 32}{res}{space 2} .7214322{col 44}{space 2} .1448102{col 55}{space 1}   -1.63{col 64}{space 3}0.104{col 72}{space 4} .4867265{col 85}{space 3} 1.069316
{txt}{space 28}4  {c |}{col 32}{res}{space 2} .9564878{col 44}{space 2} .1730604{col 55}{space 1}   -0.25{col 64}{space 3}0.806{col 72}{space 4} .6708443{col 85}{space 3} 1.363757
{txt}{space 17}Top Quintile  {c |}{col 32}{res}{space 2} 1.606635{col 44}{space 2} .2740557{col 55}{space 1}    2.78{col 64}{space 3}0.005{col 72}{space 4} 1.149945{col 85}{space 3} 2.244695
{txt}{space 30} {c |}
{space 25}_cons {c |}{col 32}{res}{space 2} .1893804{col 44}{space 2} .0292468{col 55}{space 1}  -10.77{col 64}{space 3}0.000{col 72}{space 4} .1399073{col 85}{space 3} .2563479
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit tech_improve i.equivincomeHHquintile_HBAI_imp $controls_demographics if infullmodel_TableD2 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}     10.37
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                        tech_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5046275{col 50}{space 2} .1133756{col 61}{space 1}   -3.04{col 70}{space 3}0.002{col 78}{space 4} .3248415{col 91}{space 3} .7839176
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .5393284{col 50}{space 2} .1130688{col 61}{space 1}   -2.95{col 70}{space 3}0.003{col 78}{space 4}  .357558{col 91}{space 3} .8135046
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .6527976{col 50}{space 2} .1279229{col 61}{space 1}   -2.18{col 70}{space 3}0.030{col 78}{space 4} .4445547{col 91}{space 3} .9585877
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .8106742{col 50}{space 2} .1603535{col 61}{space 1}   -1.06{col 70}{space 3}0.289{col 78}{space 4} .5500789{col 91}{space 3} 1.194724
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .5985055{col 50}{space 2} .0790013{col 61}{space 1}   -3.89{col 70}{space 3}0.000{col 78}{space 4} .4620376{col 91}{space 3} .7752808
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .4215797{col 50}{space 2} .0637814{col 61}{space 1}   -5.71{col 70}{space 3}0.000{col 78}{space 4} .3133725{col 91}{space 3} .5671507
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6601585{col 50}{space 2} .0671241{col 61}{space 1}   -4.08{col 70}{space 3}0.000{col 78}{space 4} .5408448{col 91}{space 3} .8057937
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.265467{col 50}{space 2}  .133295{col 61}{space 1}    2.24{col 70}{space 3}0.025{col 78}{space 4} 1.029353{col 91}{space 3} 1.555742
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2}  .743296{col 50}{space 2} .1110058{col 61}{space 1}   -1.99{col 70}{space 3}0.047{col 78}{space 4} .5546286{col 91}{space 3} .9961423
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .7884021{col 50}{space 2} .0885944{col 61}{space 1}   -2.12{col 70}{space 3}0.034{col 78}{space 4} .6325097{col 91}{space 3} .9827167
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .7632024{col 50}{space 2} .1782472{col 61}{space 1}   -1.16{col 70}{space 3}0.247{col 78}{space 4} .4828136{col 91}{space 3} 1.206424
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9818739{col 50}{space 2}  .171683{col 61}{space 1}   -0.10{col 70}{space 3}0.917{col 78}{space 4} .6969083{col 91}{space 3} 1.383362
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8789707{col 50}{space 2} .3416891{col 61}{space 1}   -0.33{col 70}{space 3}0.740{col 78}{space 4} .4101844{col 91}{space 3} 1.883517
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}  .783422{col 50}{space 2} .1021823{col 61}{space 1}   -1.87{col 70}{space 3}0.061{col 78}{space 4} .6066511{col 91}{space 3} 1.011702
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} .4522395{col 50}{space 2}  .074826{col 61}{space 1}   -4.80{col 70}{space 3}0.000{col 78}{space 4} .3269555{col 91}{space 3} .6255304
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .4080237{col 50}{space 2} .0803554{col 61}{space 1}   -4.55{col 70}{space 3}0.000{col 78}{space 4} .2773321{col 91}{space 3} .6003032
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .4044302{col 50}{space 2}  .082221{col 61}{space 1}   -4.45{col 70}{space 3}0.000{col 78}{space 4} .2714817{col 91}{space 3} .6024853
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .8529294{col 50}{space 2} .2005401{col 61}{space 1}   -0.68{col 70}{space 3}0.499{col 78}{space 4} .5379205{col 91}{space 3} 1.352409
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit tech_improve i.equivincomeHHquintile_HBAI_imp $controls_demographics $controls_objective if infullmodel_TableD2 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      8.76
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                        tech_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5084147{col 50}{space 2} .1134948{col 61}{space 1}   -3.03{col 70}{space 3}0.002{col 78}{space 4} .3282041{col 91}{space 3} .7875756
{txt}{space 34}3  {c |}{col 38}{res}{space 2}  .526227{col 50}{space 2} .1103904{col 61}{space 1}   -3.06{col 70}{space 3}0.002{col 78}{space 4} .3487835{col 91}{space 3} .7939448
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .6499105{col 50}{space 2} .1271704{col 61}{space 1}   -2.20{col 70}{space 3}0.028{col 78}{space 4} .4428379{col 91}{space 3} .9538108
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .7725892{col 50}{space 2} .1535353{col 61}{space 1}   -1.30{col 70}{space 3}0.194{col 78}{space 4}  .523286{col 91}{space 3} 1.140665
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .6059595{col 50}{space 2} .0801221{col 61}{space 1}   -3.79{col 70}{space 3}0.000{col 78}{space 4} .4675847{col 91}{space 3} .7852841
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .4297908{col 50}{space 2} .0649978{col 61}{space 1}   -5.58{col 70}{space 3}0.000{col 78}{space 4} .3195138{col 91}{space 3}  .578129
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6844365{col 50}{space 2} .0702137{col 61}{space 1}   -3.70{col 70}{space 3}0.000{col 78}{space 4} .5597383{col 91}{space 3} .8369149
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.259845{col 50}{space 2}  .132008{col 61}{space 1}    2.20{col 70}{space 3}0.028{col 78}{space 4} 1.025888{col 91}{space 3} 1.547157
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .7404869{col 50}{space 2} .1100479{col 61}{space 1}   -2.02{col 70}{space 3}0.043{col 78}{space 4} .5533208{col 91}{space 3} .9909639
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .8021883{col 50}{space 2} .0926664{col 61}{space 1}   -1.91{col 70}{space 3}0.056{col 78}{space 4} .6396141{col 91}{space 3} 1.006085
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .7757171{col 50}{space 2} .1779182{col 61}{space 1}   -1.11{col 70}{space 3}0.268{col 78}{space 4} .4947803{col 91}{space 3}  1.21617
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.063545{col 50}{space 2} .1872545{col 61}{space 1}    0.35{col 70}{space 3}0.726{col 78}{space 4} .7530814{col 91}{space 3} 1.501998
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .9270945{col 50}{space 2} .3635164{col 61}{space 1}   -0.19{col 70}{space 3}0.847{col 78}{space 4} .4297971{col 91}{space 3} 1.999791
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .8730948{col 50}{space 2} .1217765{col 61}{space 1}   -0.97{col 70}{space 3}0.331{col 78}{space 4} .6642051{col 91}{space 3} 1.147679
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} .6410654{col 50}{space 2} .1550765{col 61}{space 1}   -1.84{col 70}{space 3}0.066{col 78}{space 4} .3989607{col 91}{space 3} 1.030089
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .5559549{col 50}{space 2} .1282872{col 61}{space 1}   -2.54{col 70}{space 3}0.011{col 78}{space 4}  .353642{col 91}{space 3} .8740078
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .6599147{col 50}{space 2} .1820168{col 61}{space 1}   -1.51{col 70}{space 3}0.132{col 78}{space 4} .3842721{col 91}{space 3} 1.133279
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.274265{col 50}{space 2} .1638121{col 61}{space 1}    1.89{col 70}{space 3}0.059{col 78}{space 4} .9903772{col 91}{space 3} 1.639527
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.626424{col 50}{space 2}  .455619{col 61}{space 1}    1.74{col 70}{space 3}0.083{col 78}{space 4} .9390941{col 91}{space 3} 2.816815
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.166789{col 50}{space 2}  .320711{col 61}{space 1}    0.56{col 70}{space 3}0.575{col 78}{space 4} .6806977{col 91}{space 3} 2.000002
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .4380315{col 50}{space 2} .1188453{col 61}{space 1}   -3.04{col 70}{space 3}0.002{col 78}{space 4} .2573294{col 91}{space 3} .7456262
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .8297709{col 50}{space 2} .1214935{col 61}{space 1}   -1.27{col 70}{space 3}0.203{col 78}{space 4} .6227142{col 91}{space 3} 1.105675
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} .6009114{col 50}{space 2} .0793849{col 61}{space 1}   -3.86{col 70}{space 3}0.000{col 78}{space 4} .4637949{col 91}{space 3}  .778565
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .6160897{col 50}{space 2} .1882486{col 61}{space 1}   -1.59{col 70}{space 3}0.113{col 78}{space 4}  .338436{col 91}{space 3} 1.121531
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 3: Worsen v Improve
. svy: logit tech_directionworse i.equivincomeHHquintile_HBAI_imp if infullmodel_TableD2 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,324
{txt}{col 1}Number of PSUs{col 20}= {res}    1,324{txt}{col 49}Population size{col 67}={res} 1,363.3793
{txt}{col 49}Design df{col 67}= {res}     1,323
{txt}{col 49}F({res}   4{txt},{res}   1320{txt}){col 67}= {res}      6.35
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 32}{c |}{col 44}  Linearized
{col 1}           tech_directionworse{col 32}{c |} Odds Ratio{col 44}   Std. Err.{col 56}      t{col 64}   P>|t|{col 72}     [95% Con{col 85}f. Interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
equivincomeHHquintile_HBAI_imp {c |}
{space 28}2  {c |}{col 32}{res}{space 2} 2.023636{col 44}{space 2} .5288131{col 55}{space 1}    2.70{col 64}{space 3}0.007{col 72}{space 4} 1.211976{col 85}{space 3} 3.378863
{txt}{space 28}3  {c |}{col 32}{res}{space 2} 1.435373{col 44}{space 2} .3515945{col 55}{space 1}    1.48{col 64}{space 3}0.140{col 72}{space 4} .8877116{col 85}{space 3} 2.320905
{txt}{space 28}4  {c |}{col 32}{res}{space 2} 1.150205{col 44}{space 2} .2611415{col 55}{space 1}    0.62{col 64}{space 3}0.538{col 72}{space 4} .7367874{col 85}{space 3} 1.795594
{txt}{space 17}Top Quintile  {c |}{col 32}{res}{space 2} .8008659{col 44}{space 2} .1713792{col 55}{space 1}   -1.04{col 64}{space 3}0.300{col 72}{space 4} .5263109{col 85}{space 3} 1.218645
{txt}{space 30} {c |}
{space 25}_cons {c |}{col 32}{res}{space 2} .9580403{col 44}{space 2} .1823349{col 55}{space 1}   -0.23{col 64}{space 3}0.822{col 72}{space 4} .6595295{col 85}{space 3} 1.391661
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit tech_directionworse i.equivincomeHHquintile_HBAI_imp $controls_demographics if infullmodel_TableD2 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,324
{txt}{col 1}Number of PSUs{col 20}= {res}    1,324{txt}{col 49}Population size{col 67}={res} 1,363.3793
{txt}{col 49}Design df{col 67}= {res}     1,323
{txt}{col 49}F({res}  17{txt},{res}   1307{txt}){col 67}= {res}      5.64
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                 tech_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.938953{col 50}{space 2} .5305978{col 61}{space 1}    2.42{col 70}{space 3}0.016{col 78}{space 4}   1.1335{col 91}{space 3} 3.316755
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.783753{col 50}{space 2} .4656758{col 61}{space 1}    2.22{col 70}{space 3}0.027{col 78}{space 4} 1.068839{col 91}{space 3} 2.976853
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.612297{col 50}{space 2} .3969295{col 61}{space 1}    1.94{col 70}{space 3}0.053{col 78}{space 4} .9947108{col 91}{space 3} 2.613324
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.469768{col 50}{space 2} .3620708{col 61}{space 1}    1.56{col 70}{space 3}0.118{col 78}{space 4} .9064985{col 91}{space 3} 2.383036
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.943099{col 50}{space 2} .3551903{col 61}{space 1}    3.63{col 70}{space 3}0.000{col 78}{space 4} 1.357552{col 91}{space 3} 2.781208
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 2.471226{col 50}{space 2} .4922539{col 61}{space 1}    4.54{col 70}{space 3}0.000{col 78}{space 4} 1.671871{col 91}{space 3} 3.652768
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} 1.217073{col 50}{space 2}  .161402{col 61}{space 1}    1.48{col 70}{space 3}0.139{col 78}{space 4} .9382779{col 91}{space 3} 1.578709
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .9196671{col 50}{space 2} .1255281{col 61}{space 1}   -0.61{col 70}{space 3}0.540{col 78}{space 4} .7036249{col 91}{space 3} 1.202043
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.559233{col 50}{space 2} .2768503{col 61}{space 1}    2.50{col 70}{space 3}0.012{col 78}{space 4} 1.100619{col 91}{space 3} 2.208945
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.171102{col 50}{space 2} .1735922{col 61}{space 1}    1.07{col 70}{space 3}0.287{col 78}{space 4} .8755998{col 91}{space 3} 1.566331
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.160133{col 50}{space 2} .3559632{col 61}{space 1}    0.48{col 70}{space 3}0.628{col 78}{space 4} .6354692{col 91}{space 3} 2.117975
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9135928{col 50}{space 2} .2204282{col 61}{space 1}   -0.37{col 70}{space 3}0.708{col 78}{space 4} .5691018{col 91}{space 3} 1.466613
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.085455{col 50}{space 2} .4890278{col 61}{space 1}    0.18{col 70}{space 3}0.856{col 78}{space 4} .4485086{col 91}{space 3} 2.626958
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}  1.55857{col 50}{space 2} .2639934{col 61}{space 1}    2.62{col 70}{space 3}0.009{col 78}{space 4} 1.117934{col 91}{space 3} 2.172883
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 3.092465{col 50}{space 2} .6115659{col 61}{space 1}    5.71{col 70}{space 3}0.000{col 78}{space 4} 2.098056{col 91}{space 3} 4.558191
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 2.031276{col 50}{space 2} .5092101{col 61}{space 1}    2.83{col 70}{space 3}0.005{col 78}{space 4} 1.242196{col 91}{space 3} 3.321603
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 2.505983{col 50}{space 2} .6336438{col 61}{space 1}    3.63{col 70}{space 3}0.000{col 78}{space 4} 1.525993{col 91}{space 3} 4.115319
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .1993451{col 50}{space 2} .0601458{col 61}{space 1}   -5.35{col 70}{space 3}0.000{col 78}{space 4} .1102931{col 91}{space 3} .3602988
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit tech_directionworse i.equivincomeHHquintile_HBAI_imp $controls_demographics $controls_objective if infullmodel_TableD2 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,324
{txt}{col 1}Number of PSUs{col 20}= {res}    1,324{txt}{col 49}Population size{col 67}={res} 1,363.3793
{txt}{col 49}Design df{col 67}= {res}     1,323
{txt}{col 49}F({res}  23{txt},{res}   1301{txt}){col 67}= {res}      4.33
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                 tech_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.921505{col 50}{space 2} .5230416{col 61}{space 1}    2.40{col 70}{space 3}0.017{col 78}{space 4} 1.126494{col 91}{space 3} 3.277587
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.825607{col 50}{space 2} .4733348{col 61}{space 1}    2.32{col 70}{space 3}0.020{col 78}{space 4} 1.097766{col 91}{space 3} 3.036022
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.655714{col 50}{space 2} .4066966{col 61}{space 1}    2.05{col 70}{space 3}0.040{col 78}{space 4} 1.022613{col 91}{space 3} 2.680768
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.503107{col 50}{space 2} .3684373{col 61}{space 1}    1.66{col 70}{space 3}0.097{col 78}{space 4} .9292974{col 91}{space 3} 2.431224
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.938778{col 50}{space 2} .3521668{col 61}{space 1}    3.64{col 70}{space 3}0.000{col 78}{space 4} 1.357598{col 91}{space 3} 2.768758
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 2.417136{col 50}{space 2} .4802911{col 61}{space 1}    4.44{col 70}{space 3}0.000{col 78}{space 4} 1.636856{col 91}{space 3} 3.569373
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} 1.183359{col 50}{space 2} .1598184{col 61}{space 1}    1.25{col 70}{space 3}0.213{col 78}{space 4} .9079297{col 91}{space 3} 1.542342
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .9272337{col 50}{space 2} .1274089{col 61}{space 1}   -0.55{col 70}{space 3}0.583{col 78}{space 4} .7081424{col 91}{space 3} 1.214109
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.554239{col 50}{space 2} .2761429{col 61}{space 1}    2.48{col 70}{space 3}0.013{col 78}{space 4} 1.096846{col 91}{space 3} 2.202369
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.211282{col 50}{space 2} .1837786{col 61}{space 1}    1.26{col 70}{space 3}0.207{col 78}{space 4} .8994571{col 91}{space 3} 1.631209
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.204785{col 50}{space 2} .3707448{col 61}{space 1}    0.61{col 70}{space 3}0.545{col 78}{space 4} .6587672{col 91}{space 3} 2.203368
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .8523103{col 50}{space 2} .2068088{col 61}{space 1}   -0.66{col 70}{space 3}0.510{col 78}{space 4} .5295036{col 91}{space 3} 1.371913
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.041189{col 50}{space 2} .4682696{col 61}{space 1}    0.09{col 70}{space 3}0.929{col 78}{space 4} .4308787{col 91}{space 3}  2.51596
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.306476{col 50}{space 2} .2449688{col 61}{space 1}    1.43{col 70}{space 3}0.154{col 78}{space 4} .9043825{col 91}{space 3} 1.887343
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.758551{col 50}{space 2} .5149379{col 61}{space 1}    1.93{col 70}{space 3}0.054{col 78}{space 4} .9900981{col 91}{space 3} 3.123428
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.688526{col 50}{space 2} .4890938{col 61}{space 1}    1.81{col 70}{space 3}0.071{col 78}{space 4} .9565887{col 91}{space 3} 2.980508
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 2.096836{col 50}{space 2} .6928849{col 61}{space 1}    2.24{col 70}{space 3}0.025{col 78}{space 4} 1.096563{col 91}{space 3} 4.009551
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .8637467{col 50}{space 2} .1494429{col 61}{space 1}   -0.85{col 70}{space 3}0.397{col 78}{space 4} .6151474{col 91}{space 3} 1.212812
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} .8687686{col 50}{space 2} .3100844{col 61}{space 1}   -0.39{col 70}{space 3}0.694{col 78}{space 4}  .431332{col 91}{space 3} 1.749833
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} .9420662{col 50}{space 2} .3345992{col 61}{space 1}   -0.17{col 70}{space 3}0.867{col 78}{space 4} .4693299{col 91}{space 3}  1.89097
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2}  2.44301{col 50}{space 2} .8119485{col 61}{space 1}    2.69{col 70}{space 3}0.007{col 78}{space 4} 1.272812{col 91}{space 3} 4.689063
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.033409{col 50}{space 2} .2232271{col 61}{space 1}    0.15{col 70}{space 3}0.879{col 78}{space 4} .6764497{col 91}{space 3} 1.578734
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.559699{col 50}{space 2} .2770991{col 61}{space 1}    2.50{col 70}{space 3}0.012{col 78}{space 4} 1.100719{col 91}{space 3} 2.210067
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2127314{col 50}{space 2} .0828209{col 61}{space 1}   -3.98{col 70}{space 3}0.000{col 78}{space 4} .0991145{col 91}{space 3} .4565899
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. ******* Employment Status ********
. 
. 
. * Model 1: Worsen 
. svy: logit tech_worsen ib2.employmentstatus5 if infullmodel_TableD2 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   1{txt},{res}   3962{txt}){col 67}= {res}      1.28
{txt}{col 49}Prob > F{col 67}= {res}    0.2578

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}  Linearized
{col 1}      tech_worsen{col 19}{c |} Odds Ratio{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
employmentstatus5 {c |}
{space 7}FT Worker  {c |}{col 19}{res}{space 2} .8907007{col 31}{space 2} .0910862{col 42}{space 1}   -1.13{col 51}{space 3}0.258{col 59}{space 4} .7288839{col 72}{space 3} 1.088442
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} .2248846{col 31}{space 2}   .01946{col 42}{space 1}  -17.24{col 51}{space 3}0.000{col 59}{space 4} .1897928{col 72}{space 3} .2664646
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit tech_worsen ib2.employmentstatus5 $controls_demographics if infullmodel_TableD2 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      2.51
{txt}{col 49}Prob > F{col 67}= {res}    0.0006

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                         tech_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} .7937762{col 50}{space 2} .0921161{col 61}{space 1}   -1.99{col 70}{space 3}0.047{col 78}{space 4} .6322483{col 91}{space 3} .9965715
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.288334{col 50}{space 2} .1934766{col 61}{space 1}    1.69{col 70}{space 3}0.092{col 78}{space 4} .9597529{col 91}{space 3} 1.729408
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.262694{col 50}{space 2} .2013064{col 61}{space 1}    1.46{col 70}{space 3}0.144{col 78}{space 4} .9237462{col 91}{space 3}  1.72601
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .8669435{col 50}{space 2} .0848512{col 61}{space 1}   -1.46{col 70}{space 3}0.145{col 78}{space 4} .7155746{col 91}{space 3} 1.050332
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.110653{col 50}{space 2} .1160975{col 61}{space 1}    1.00{col 70}{space 3}0.315{col 78}{space 4} .9048459{col 91}{space 3} 1.363272
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9847405{col 50}{space 2} .1058998{col 61}{space 1}   -0.14{col 70}{space 3}0.886{col 78}{space 4} .7975444{col 91}{space 3} 1.215874
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9241554{col 50}{space 2} .1945524{col 61}{space 1}   -0.37{col 70}{space 3}0.708{col 78}{space 4} .6116401{col 91}{space 3} 1.396349
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .8155661{col 50}{space 2} .1400316{col 61}{space 1}   -1.19{col 70}{space 3}0.235{col 78}{space 4} .5824577{col 91}{space 3} 1.141968
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .9556425{col 50}{space 2} .2910917{col 61}{space 1}   -0.15{col 70}{space 3}0.882{col 78}{space 4} .5259417{col 91}{space 3} 1.736414
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.167665{col 50}{space 2} .2125261{col 61}{space 1}    0.85{col 70}{space 3}0.394{col 78}{space 4} .8172294{col 91}{space 3}  1.66837
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.069154{col 50}{space 2}  .189471{col 61}{space 1}    0.38{col 70}{space 3}0.706{col 78}{space 4} .7553496{col 91}{space 3} 1.513327
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.117951{col 50}{space 2} .1984927{col 61}{space 1}    0.63{col 70}{space 3}0.530{col 78}{space 4} .7893058{col 91}{space 3} 1.583435
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.264681{col 50}{space 2}  .224145{col 61}{space 1}    1.32{col 70}{space 3}0.185{col 78}{space 4} .8934554{col 91}{space 3}  1.79015
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.282489{col 50}{space 2} .1698614{col 61}{space 1}    1.88{col 70}{space 3}0.060{col 78}{space 4} .9891918{col 91}{space 3}  1.66275
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.814161{col 50}{space 2} .2435089{col 61}{space 1}    4.44{col 70}{space 3}0.000{col 78}{space 4} 1.394398{col 91}{space 3} 2.360287
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .8573966{col 50}{space 2} .1444471{col 61}{space 1}   -0.91{col 70}{space 3}0.361{col 78}{space 4} .6162182{col 91}{space 3} 1.192968
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.127179{col 50}{space 2} .2059426{col 61}{space 1}    0.66{col 70}{space 3}0.512{col 78}{space 4} .7878174{col 91}{space 3} 1.612725
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .1572991{col 50}{space 2} .0368279{col 61}{space 1}   -7.90{col 70}{space 3}0.000{col 78}{space 4} .0993977{col 91}{space 3} .2489292
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit tech_worsen ib2.employmentstatus5 $controls_demographics $controls_objective if infullmodel_TableD2 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      2.57
{txt}{col 49}Prob > F{col 67}= {res}    0.0001

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                         tech_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2}  .801175{col 50}{space 2} .0944525{col 61}{space 1}   -1.88{col 70}{space 3}0.060{col 78}{space 4}  .635838{col 91}{space 3} 1.009505
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.316433{col 50}{space 2} .1963717{col 61}{space 1}    1.84{col 70}{space 3}0.065{col 78}{space 4}  .982622{col 91}{space 3} 1.763644
{txt}{space 32}50+  {c |}{col 38}{res}{space 2}  1.29173{col 50}{space 2} .2050114{col 61}{space 1}    1.61{col 70}{space 3}0.107{col 78}{space 4} .9463145{col 91}{space 3} 1.763225
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}  .868358{col 50}{space 2} .0862481{col 61}{space 1}   -1.42{col 70}{space 3}0.155{col 78}{space 4} .7147084{col 91}{space 3} 1.055039
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  1.10359{col 50}{space 2}  .116495{col 61}{space 1}    0.93{col 70}{space 3}0.350{col 78}{space 4} .8972786{col 91}{space 3} 1.357338
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.037156{col 50}{space 2} .1137139{col 61}{space 1}    0.33{col 70}{space 3}0.739{col 78}{space 4} .8365462{col 91}{space 3} 1.285874
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9606719{col 50}{space 2} .2051961{col 61}{space 1}   -0.19{col 70}{space 3}0.851{col 78}{space 4} .6319838{col 91}{space 3} 1.460307
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .8244381{col 50}{space 2} .1417613{col 61}{space 1}   -1.12{col 70}{space 3}0.262{col 78}{space 4} .5885049{col 91}{space 3} 1.154958
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2}  .951978{col 50}{space 2} .2910022{col 61}{space 1}   -0.16{col 70}{space 3}0.872{col 78}{space 4} .5228182{col 91}{space 3} 1.733417
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.161317{col 50}{space 2} .2124477{col 61}{space 1}    0.82{col 70}{space 3}0.414{col 78}{space 4} .8113102{col 91}{space 3} 1.662319
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.074199{col 50}{space 2} .1910727{col 61}{space 1}    0.40{col 70}{space 3}0.687{col 78}{space 4} .7579338{col 91}{space 3} 1.522433
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.133929{col 50}{space 2} .2019321{col 61}{space 1}    0.71{col 70}{space 3}0.480{col 78}{space 4} .7997533{col 91}{space 3} 1.607739
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.255271{col 50}{space 2} .2217886{col 61}{space 1}    1.29{col 70}{space 3}0.198{col 78}{space 4} .8877618{col 91}{space 3}  1.77492
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.145882{col 50}{space 2} .1645278{col 61}{space 1}    0.95{col 70}{space 3}0.343{col 78}{space 4} .8647401{col 91}{space 3} 1.518428
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.295141{col 50}{space 2} .2747104{col 61}{space 1}    1.22{col 70}{space 3}0.223{col 78}{space 4}  .854505{col 91}{space 3} 1.962995
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .9488207{col 50}{space 2} .1787633{col 61}{space 1}   -0.28{col 70}{space 3}0.780{col 78}{space 4} .6557886{col 91}{space 3} 1.372791
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.531672{col 50}{space 2} .3715764{col 61}{space 1}    1.76{col 70}{space 3}0.079{col 78}{space 4} .9519308{col 91}{space 3} 2.464486
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .9881793{col 50}{space 2} .1211732{col 61}{space 1}   -0.10{col 70}{space 3}0.923{col 78}{space 4} .7770111{col 91}{space 3} 1.256737
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.529412{col 50}{space 2} .4135439{col 61}{space 1}    1.57{col 70}{space 3}0.116{col 78}{space 4} .9001072{col 91}{space 3} 2.598691
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.281864{col 50}{space 2} .3432878{col 61}{space 1}    0.93{col 70}{space 3}0.354{col 78}{space 4} .7582589{col 91}{space 3} 2.167038
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} 1.394314{col 50}{space 2} .3512092{col 61}{space 1}    1.32{col 70}{space 3}0.187{col 78}{space 4} .8509197{col 91}{space 3} 2.284717
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .8428629{col 50}{space 2} .1464735{col 61}{space 1}   -0.98{col 70}{space 3}0.325{col 78}{space 4} .5994999{col 91}{space 3} 1.185017
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.179514{col 50}{space 2} .1735202{col 61}{space 1}    1.12{col 70}{space 3}0.262{col 78}{space 4} .8839814{col 91}{space 3} 1.573849
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .1072243{col 50}{space 2} .0328271{col 61}{space 1}   -7.29{col 70}{space 3}0.000{col 78}{space 4} .0588322{col 91}{space 3} .1954211
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 2: Improve 
. svy: logit tech_improve ib2.employmentstatus5 if infullmodel_TableD2 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   1{txt},{res}   3962{txt}){col 67}= {res}     30.34
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}  Linearized
{col 1}     tech_improve{col 19}{c |} Odds Ratio{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
employmentstatus5 {c |}
{space 7}FT Worker  {c |}{col 19}{res}{space 2} 2.053766{col 31}{space 2}  .268355{col 42}{space 1}    5.51{col 51}{space 3}0.000{col 59}{space 4} 1.589626{col 72}{space 3} 2.653427
{txt}{space 12}_cons {c |}{col 19}{res}{space 2}  .109384{col 31}{space 2} .0131425{col 42}{space 1}  -18.42{col 51}{space 3}0.000{col 59}{space 4} .0864273{col 72}{space 3} .1384386
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit tech_improve ib2.employmentstatus5 $controls_demographics if infullmodel_TableD2 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}     10.37
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                        tech_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} 1.345359{col 50}{space 2} .2009195{col 61}{space 1}    1.99{col 70}{space 3}0.047{col 78}{space 4} 1.003873{col 91}{space 3} 1.803008
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .5985055{col 50}{space 2} .0790013{col 61}{space 1}   -3.89{col 70}{space 3}0.000{col 78}{space 4} .4620376{col 91}{space 3} .7752808
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .4215797{col 50}{space 2} .0637814{col 61}{space 1}   -5.71{col 70}{space 3}0.000{col 78}{space 4} .3133725{col 91}{space 3} .5671507
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6601585{col 50}{space 2} .0671241{col 61}{space 1}   -4.08{col 70}{space 3}0.000{col 78}{space 4} .5408448{col 91}{space 3} .8057937
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.265467{col 50}{space 2}  .133295{col 61}{space 1}    2.24{col 70}{space 3}0.025{col 78}{space 4} 1.029353{col 91}{space 3} 1.555742
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .7884021{col 50}{space 2} .0885944{col 61}{space 1}   -2.12{col 70}{space 3}0.034{col 78}{space 4} .6325097{col 91}{space 3} .9827167
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .7632024{col 50}{space 2} .1782472{col 61}{space 1}   -1.16{col 70}{space 3}0.247{col 78}{space 4} .4828136{col 91}{space 3} 1.206424
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9818739{col 50}{space 2}  .171683{col 61}{space 1}   -0.10{col 70}{space 3}0.917{col 78}{space 4} .6969083{col 91}{space 3} 1.383362
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .8789707{col 50}{space 2} .3416891{col 61}{space 1}   -0.33{col 70}{space 3}0.740{col 78}{space 4} .4101844{col 91}{space 3} 1.883517
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5046275{col 50}{space 2} .1133756{col 61}{space 1}   -3.04{col 70}{space 3}0.002{col 78}{space 4} .3248415{col 91}{space 3} .7839176
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .5393284{col 50}{space 2} .1130688{col 61}{space 1}   -2.95{col 70}{space 3}0.003{col 78}{space 4}  .357558{col 91}{space 3} .8135046
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .6527976{col 50}{space 2} .1279229{col 61}{space 1}   -2.18{col 70}{space 3}0.030{col 78}{space 4} .4445547{col 91}{space 3} .9585877
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .8106742{col 50}{space 2} .1603535{col 61}{space 1}   -1.06{col 70}{space 3}0.289{col 78}{space 4} .5500789{col 91}{space 3} 1.194724
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}  .783422{col 50}{space 2} .1021823{col 61}{space 1}   -1.87{col 70}{space 3}0.061{col 78}{space 4} .6066511{col 91}{space 3} 1.011702
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} .4522395{col 50}{space 2}  .074826{col 61}{space 1}   -4.80{col 70}{space 3}0.000{col 78}{space 4} .3269555{col 91}{space 3} .6255304
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .4080237{col 50}{space 2} .0803554{col 61}{space 1}   -4.55{col 70}{space 3}0.000{col 78}{space 4} .2773321{col 91}{space 3} .6003032
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .4044302{col 50}{space 2}  .082221{col 61}{space 1}   -4.45{col 70}{space 3}0.000{col 78}{space 4} .2714817{col 91}{space 3} .6024853
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2}  .633979{col 50}{space 2} .1640938{col 61}{space 1}   -1.76{col 70}{space 3}0.078{col 78}{space 4} .3816715{col 91}{space 3} 1.053077
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit tech_improve ib2.employmentstatus5 $controls_demographics $controls_objective if infullmodel_TableD2 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      8.76
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                        tech_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} 1.350463{col 50}{space 2} .2006997{col 61}{space 1}    2.02{col 70}{space 3}0.043{col 78}{space 4} 1.009118{col 91}{space 3}  1.80727
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .6059595{col 50}{space 2} .0801221{col 61}{space 1}   -3.79{col 70}{space 3}0.000{col 78}{space 4} .4675847{col 91}{space 3} .7852841
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .4297908{col 50}{space 2} .0649978{col 61}{space 1}   -5.58{col 70}{space 3}0.000{col 78}{space 4} .3195138{col 91}{space 3}  .578129
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6844365{col 50}{space 2} .0702137{col 61}{space 1}   -3.70{col 70}{space 3}0.000{col 78}{space 4} .5597383{col 91}{space 3} .8369149
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.259845{col 50}{space 2}  .132008{col 61}{space 1}    2.20{col 70}{space 3}0.028{col 78}{space 4} 1.025888{col 91}{space 3} 1.547157
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .8021883{col 50}{space 2} .0926664{col 61}{space 1}   -1.91{col 70}{space 3}0.056{col 78}{space 4} .6396141{col 91}{space 3} 1.006085
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .7757171{col 50}{space 2} .1779182{col 61}{space 1}   -1.11{col 70}{space 3}0.268{col 78}{space 4} .4947803{col 91}{space 3}  1.21617
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.063545{col 50}{space 2} .1872545{col 61}{space 1}    0.35{col 70}{space 3}0.726{col 78}{space 4} .7530814{col 91}{space 3} 1.501998
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .9270945{col 50}{space 2} .3635164{col 61}{space 1}   -0.19{col 70}{space 3}0.847{col 78}{space 4} .4297971{col 91}{space 3} 1.999791
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .5084147{col 50}{space 2} .1134948{col 61}{space 1}   -3.03{col 70}{space 3}0.002{col 78}{space 4} .3282041{col 91}{space 3} .7875756
{txt}{space 34}3  {c |}{col 38}{res}{space 2}  .526227{col 50}{space 2} .1103904{col 61}{space 1}   -3.06{col 70}{space 3}0.002{col 78}{space 4} .3487835{col 91}{space 3} .7939448
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .6499105{col 50}{space 2} .1271704{col 61}{space 1}   -2.20{col 70}{space 3}0.028{col 78}{space 4} .4428379{col 91}{space 3} .9538108
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .7725892{col 50}{space 2} .1535353{col 61}{space 1}   -1.30{col 70}{space 3}0.194{col 78}{space 4}  .523286{col 91}{space 3} 1.140665
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .8730948{col 50}{space 2} .1217765{col 61}{space 1}   -0.97{col 70}{space 3}0.331{col 78}{space 4} .6642051{col 91}{space 3} 1.147679
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} .6410654{col 50}{space 2} .1550765{col 61}{space 1}   -1.84{col 70}{space 3}0.066{col 78}{space 4} .3989607{col 91}{space 3} 1.030089
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .5559549{col 50}{space 2} .1282872{col 61}{space 1}   -2.54{col 70}{space 3}0.011{col 78}{space 4}  .353642{col 91}{space 3} .8740078
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .6599147{col 50}{space 2} .1820168{col 61}{space 1}   -1.51{col 70}{space 3}0.132{col 78}{space 4} .3842721{col 91}{space 3} 1.133279
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.274265{col 50}{space 2} .1638121{col 61}{space 1}    1.89{col 70}{space 3}0.059{col 78}{space 4} .9903772{col 91}{space 3} 1.639527
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.626424{col 50}{space 2}  .455619{col 61}{space 1}    1.74{col 70}{space 3}0.083{col 78}{space 4} .9390941{col 91}{space 3} 2.816815
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.166789{col 50}{space 2}  .320711{col 61}{space 1}    0.56{col 70}{space 3}0.575{col 78}{space 4} .6806977{col 91}{space 3} 2.000002
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .4380315{col 50}{space 2} .1188453{col 61}{space 1}   -3.04{col 70}{space 3}0.002{col 78}{space 4} .2573294{col 91}{space 3} .7456262
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .8297709{col 50}{space 2} .1214935{col 61}{space 1}   -1.27{col 70}{space 3}0.203{col 78}{space 4} .6227142{col 91}{space 3} 1.105675
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} .6009114{col 50}{space 2} .0793849{col 61}{space 1}   -3.86{col 70}{space 3}0.000{col 78}{space 4} .4637949{col 91}{space 3}  .778565
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .4562064{col 50}{space 2} .1494948{col 61}{space 1}   -2.39{col 70}{space 3}0.017{col 78}{space 4} .2399633{col 91}{space 3}  .867317
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 3: Worsen v Improve
. svy: logit tech_directionworse ib2.employmentstatus5 if infullmodel_TableD2 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,324
{txt}{col 1}Number of PSUs{col 20}= {res}    1,324{txt}{col 49}Population size{col 67}={res} 1,363.3793
{txt}{col 49}Design df{col 67}= {res}     1,323
{txt}{col 49}F({res}   1{txt},{res}   1323{txt}){col 67}= {res}     21.55
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}  Linearized
{col 1}tech_directionworse{col 21}{c |} Odds Ratio{col 33}   Std. Err.{col 45}      t{col 53}   P>|t|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}employmentstatus5 {c |}
{space 9}FT Worker  {c |}{col 21}{res}{space 2} .4885558{col 33}{space 2} .0753804{col 44}{space 1}   -4.64{col 53}{space 3}0.000{col 61}{space 4} .3609608{col 74}{space 3} .6612539
{txt}{space 14}_cons {c |}{col 21}{res}{space 2} 1.862055{col 33}{space 2}  .257849{col 44}{space 1}    4.49{col 53}{space 3}0.000{col 61}{space 4} 1.419104{col 74}{space 3} 2.443268
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit tech_directionworse ib2.employmentstatus5 $controls_demographics if infullmodel_TableD2 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,324
{txt}{col 1}Number of PSUs{col 20}= {res}    1,324{txt}{col 49}Population size{col 67}={res} 1,363.3793
{txt}{col 49}Design df{col 67}= {res}     1,323
{txt}{col 49}F({res}  17{txt},{res}   1307{txt}){col 67}= {res}      5.64
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                 tech_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2}  .641341{col 50}{space 2} .1138736{col 61}{space 1}   -2.50{col 70}{space 3}0.012{col 78}{space 4} .4527048{col 91}{space 3} .9085794
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.943099{col 50}{space 2} .3551903{col 61}{space 1}    3.63{col 70}{space 3}0.000{col 78}{space 4} 1.357552{col 91}{space 3} 2.781208
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 2.471226{col 50}{space 2} .4922539{col 61}{space 1}    4.54{col 70}{space 3}0.000{col 78}{space 4} 1.671871{col 91}{space 3} 3.652768
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} 1.217073{col 50}{space 2}  .161402{col 61}{space 1}    1.48{col 70}{space 3}0.139{col 78}{space 4} .9382779{col 91}{space 3} 1.578709
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .9196671{col 50}{space 2} .1255281{col 61}{space 1}   -0.61{col 70}{space 3}0.540{col 78}{space 4} .7036249{col 91}{space 3} 1.202043
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.171102{col 50}{space 2} .1735922{col 61}{space 1}    1.07{col 70}{space 3}0.287{col 78}{space 4} .8755998{col 91}{space 3} 1.566331
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.160133{col 50}{space 2} .3559632{col 61}{space 1}    0.48{col 70}{space 3}0.628{col 78}{space 4} .6354692{col 91}{space 3} 2.117975
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9135928{col 50}{space 2} .2204282{col 61}{space 1}   -0.37{col 70}{space 3}0.708{col 78}{space 4} .5691018{col 91}{space 3} 1.466613
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.085455{col 50}{space 2} .4890278{col 61}{space 1}    0.18{col 70}{space 3}0.856{col 78}{space 4} .4485086{col 91}{space 3} 2.626958
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.938953{col 50}{space 2} .5305978{col 61}{space 1}    2.42{col 70}{space 3}0.016{col 78}{space 4}   1.1335{col 91}{space 3} 3.316755
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.783753{col 50}{space 2} .4656758{col 61}{space 1}    2.22{col 70}{space 3}0.027{col 78}{space 4} 1.068839{col 91}{space 3} 2.976853
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.612297{col 50}{space 2} .3969295{col 61}{space 1}    1.94{col 70}{space 3}0.053{col 78}{space 4} .9947108{col 91}{space 3} 2.613324
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.469768{col 50}{space 2} .3620708{col 61}{space 1}    1.56{col 70}{space 3}0.118{col 78}{space 4} .9064985{col 91}{space 3} 2.383036
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}  1.55857{col 50}{space 2} .2639934{col 61}{space 1}    2.62{col 70}{space 3}0.009{col 78}{space 4} 1.117934{col 91}{space 3} 2.172883
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 3.092465{col 50}{space 2} .6115659{col 61}{space 1}    5.71{col 70}{space 3}0.000{col 78}{space 4} 2.098056{col 91}{space 3} 4.558191
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 2.031276{col 50}{space 2} .5092101{col 61}{space 1}    2.83{col 70}{space 3}0.005{col 78}{space 4} 1.242196{col 91}{space 3} 3.321603
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 2.505983{col 50}{space 2} .6336438{col 61}{space 1}    3.63{col 70}{space 3}0.000{col 78}{space 4} 1.525993{col 91}{space 3} 4.115319
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .3108254{col 50}{space 2} .1016707{col 61}{space 1}   -3.57{col 70}{space 3}0.000{col 78}{space 4} .1636193{col 91}{space 3} .5904711
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit tech_directionworse ib2.employmentstatus5 $controls_demographics $controls_objective if infullmodel_TableD2 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     1,324
{txt}{col 1}Number of PSUs{col 20}= {res}    1,324{txt}{col 49}Population size{col 67}={res} 1,363.3793
{txt}{col 49}Design df{col 67}= {res}     1,323
{txt}{col 49}F({res}  23{txt},{res}   1301{txt}){col 67}= {res}      4.33
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                 tech_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} .6434015{col 50}{space 2} .1143136{col 61}{space 1}   -2.48{col 70}{space 3}0.013{col 78}{space 4} .4540566{col 91}{space 3} .9117047
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.938778{col 50}{space 2} .3521668{col 61}{space 1}    3.64{col 70}{space 3}0.000{col 78}{space 4} 1.357598{col 91}{space 3} 2.768758
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 2.417136{col 50}{space 2} .4802911{col 61}{space 1}    4.44{col 70}{space 3}0.000{col 78}{space 4} 1.636856{col 91}{space 3} 3.569373
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} 1.183359{col 50}{space 2} .1598184{col 61}{space 1}    1.25{col 70}{space 3}0.213{col 78}{space 4} .9079297{col 91}{space 3} 1.542342
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .9272337{col 50}{space 2} .1274089{col 61}{space 1}   -0.55{col 70}{space 3}0.583{col 78}{space 4} .7081424{col 91}{space 3} 1.214109
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.211282{col 50}{space 2} .1837786{col 61}{space 1}    1.26{col 70}{space 3}0.207{col 78}{space 4} .8994571{col 91}{space 3} 1.631209
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.204785{col 50}{space 2} .3707448{col 61}{space 1}    0.61{col 70}{space 3}0.545{col 78}{space 4} .6587672{col 91}{space 3} 2.203368
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .8523103{col 50}{space 2} .2068088{col 61}{space 1}   -0.66{col 70}{space 3}0.510{col 78}{space 4} .5295036{col 91}{space 3} 1.371913
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.041189{col 50}{space 2} .4682696{col 61}{space 1}    0.09{col 70}{space 3}0.929{col 78}{space 4} .4308787{col 91}{space 3}  2.51596
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.921505{col 50}{space 2} .5230416{col 61}{space 1}    2.40{col 70}{space 3}0.017{col 78}{space 4} 1.126494{col 91}{space 3} 3.277587
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.825607{col 50}{space 2} .4733348{col 61}{space 1}    2.32{col 70}{space 3}0.020{col 78}{space 4} 1.097766{col 91}{space 3} 3.036022
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.655714{col 50}{space 2} .4066966{col 61}{space 1}    2.05{col 70}{space 3}0.040{col 78}{space 4} 1.022613{col 91}{space 3} 2.680768
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.503107{col 50}{space 2} .3684373{col 61}{space 1}    1.66{col 70}{space 3}0.097{col 78}{space 4} .9292974{col 91}{space 3} 2.431224
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.306476{col 50}{space 2} .2449688{col 61}{space 1}    1.43{col 70}{space 3}0.154{col 78}{space 4} .9043825{col 91}{space 3} 1.887343
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.758551{col 50}{space 2} .5149379{col 61}{space 1}    1.93{col 70}{space 3}0.054{col 78}{space 4} .9900981{col 91}{space 3} 3.123428
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.688526{col 50}{space 2} .4890938{col 61}{space 1}    1.81{col 70}{space 3}0.071{col 78}{space 4} .9565887{col 91}{space 3} 2.980508
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 2.096836{col 50}{space 2} .6928849{col 61}{space 1}    2.24{col 70}{space 3}0.025{col 78}{space 4} 1.096563{col 91}{space 3} 4.009551
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .8637467{col 50}{space 2} .1494429{col 61}{space 1}   -0.85{col 70}{space 3}0.397{col 78}{space 4} .6151474{col 91}{space 3} 1.212812
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} .8687686{col 50}{space 2} .3100844{col 61}{space 1}   -0.39{col 70}{space 3}0.694{col 78}{space 4}  .431332{col 91}{space 3} 1.749833
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} .9420662{col 50}{space 2} .3345992{col 61}{space 1}   -0.17{col 70}{space 3}0.867{col 78}{space 4} .4693299{col 91}{space 3}  1.89097
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2}  2.44301{col 50}{space 2} .8119485{col 61}{space 1}    2.69{col 70}{space 3}0.007{col 78}{space 4} 1.272812{col 91}{space 3} 4.689063
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.033409{col 50}{space 2} .2232271{col 61}{space 1}    0.15{col 70}{space 3}0.879{col 78}{space 4} .6764497{col 91}{space 3} 1.578734
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.559699{col 50}{space 2} .2770991{col 61}{space 1}    2.50{col 70}{space 3}0.012{col 78}{space 4} 1.100719{col 91}{space 3} 2.210067
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .3306356{col 50}{space 2} .1353707{col 61}{space 1}   -2.70{col 70}{space 3}0.007{col 78}{space 4} .1480904{col 91}{space 3} .7381973
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
.         
.         
.         
.         
. ************* TABLE D3: Demographic Predictors of Subjective Exposure to Cheap Imports ***************
. 
. svy: logit import_worsen aiexposure_felten2021  $controls_demographics $controls_objective, or
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}note: aiexposure_felten2021 omitted because of collinearity

Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      3.43
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                       import_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .7500211{col 50}{space 2} .1116783{col 61}{space 1}   -1.93{col 70}{space 3}0.053{col 78}{space 4} .5601323{col 91}{space 3} 1.004284
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2}  1.14489{col 50}{space 2} .2193418{col 61}{space 1}    0.71{col 70}{space 3}0.480{col 78}{space 4} .7863899{col 91}{space 3} 1.666824
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.091017{col 50}{space 2} .2200251{col 61}{space 1}    0.43{col 70}{space 3}0.666{col 78}{space 4} .7347125{col 91}{space 3} 1.620114
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}  .652949{col 50}{space 2} .0838384{col 61}{space 1}   -3.32{col 70}{space 3}0.001{col 78}{space 4} .5076354{col 91}{space 3} .8398595
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.106137{col 50}{space 2} .1468444{col 61}{space 1}    0.76{col 70}{space 3}0.447{col 78}{space 4}  .852656{col 91}{space 3} 1.434974
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .8677224{col 50}{space 2} .1436729{col 61}{space 1}   -0.86{col 70}{space 3}0.392{col 78}{space 4} .6271918{col 91}{space 3} 1.200498
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .5892368{col 50}{space 2} .0900676{col 61}{space 1}   -3.46{col 70}{space 3}0.001{col 78}{space 4} .4366566{col 91}{space 3} .7951329
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}  .849199{col 50}{space 2} .2281017{col 61}{space 1}   -0.61{col 70}{space 3}0.543{col 78}{space 4} .5015336{col 91}{space 3} 1.437868
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.072952{col 50}{space 2} .2137632{col 61}{space 1}    0.35{col 70}{space 3}0.724{col 78}{space 4} .7260127{col 91}{space 3} 1.585682
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5238108{col 50}{space 2} .2125478{col 61}{space 1}   -1.59{col 70}{space 3}0.111{col 78}{space 4}  .236415{col 91}{space 3} 1.160577
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .8804051{col 50}{space 2} .1915187{col 61}{space 1}   -0.59{col 70}{space 3}0.558{col 78}{space 4} .5747249{col 91}{space 3} 1.348668
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .6408041{col 50}{space 2} .1391445{col 61}{space 1}   -2.05{col 70}{space 3}0.040{col 78}{space 4} .4186379{col 91}{space 3} .9808713
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .7738245{col 50}{space 2} .1608112{col 61}{space 1}   -1.23{col 70}{space 3}0.217{col 78}{space 4} .5148696{col 91}{space 3} 1.163022
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .5645058{col 50}{space 2} .1274512{col 61}{space 1}   -2.53{col 70}{space 3}0.011{col 78}{space 4} .3626017{col 91}{space 3} .8788345
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.240085{col 50}{space 2} .2334789{col 61}{space 1}    1.14{col 70}{space 3}0.253{col 78}{space 4} .8573168{col 91}{space 3} 1.793749
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.238847{col 50}{space 2} .3908825{col 61}{space 1}    0.68{col 70}{space 3}0.497{col 78}{space 4} .6673647{col 91}{space 3} 2.299703
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.287317{col 50}{space 2} .2907146{col 61}{space 1}    1.12{col 70}{space 3}0.263{col 78}{space 4} .8267987{col 91}{space 3} 2.004338
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 2.010093{col 50}{space 2} .5449394{col 61}{space 1}    2.58{col 70}{space 3}0.010{col 78}{space 4} 1.181364{col 91}{space 3} 3.420178
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2}        1{col 50}{txt}  (omitted)
{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 2.376303{col 50}{space 2} .7231256{col 61}{space 1}    2.84{col 70}{space 3}0.004{col 78}{space 4} 1.308567{col 91}{space 3} 4.315265
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 2.171363{col 50}{space 2} .6616116{col 61}{space 1}    2.54{col 70}{space 3}0.011{col 78}{space 4} 1.194794{col 91}{space 3} 3.946133
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .8999696{col 50}{space 2} .3310424{col 61}{space 1}   -0.29{col 70}{space 3}0.774{col 78}{space 4} .4375508{col 91}{space 3} 1.851088
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.086634{col 50}{space 2} .2196671{col 61}{space 1}    0.41{col 70}{space 3}0.681{col 78}{space 4}  .731067{col 91}{space 3} 1.615137
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} .9521414{col 50}{space 2} .1698551{col 61}{space 1}   -0.27{col 70}{space 3}0.783{col 78}{space 4} .6711304{col 91}{space 3} 1.350815
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .0978468{col 50}{space 2} .0364748{col 61}{space 1}   -6.24{col 70}{space 3}0.000{col 78}{space 4} .0471133{col 91}{space 3} .2032123
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. generate infullmodel_TableD3 = e(sample) 
{txt}
{com}.          
.          
. ******* Age Group ********
. 
. * Model 1: Worsen 
. svy: logit import_worsen ib3.age_3 if infullmodel_TableD3 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   2{txt},{res}   3961{txt}){col 67}= {res}      0.45
{txt}{col 49}Prob > F{col 67}= {res}    0.6362

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}  Linearized
{col 1}import_worsen{col 15}{c |} Odds Ratio{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}age_3group {c |}
{space 7}18-29  {c |}{col 15}{res}{space 2} .8353042{col 27}{space 2} .1581169{col 38}{space 1}   -0.95{col 47}{space 3}0.342{col 55}{space 4} .5763275{col 68}{space 3} 1.210654
{txt}{space 7}30-49  {c |}{col 15}{res}{space 2} .9599191{col 27}{space 2} .1213117{col 38}{space 1}   -0.32{col 47}{space 3}0.746{col 55}{space 4} .7492547{col 68}{space 3} 1.229815
{txt}{space 13} {c |}
{space 8}_cons {c |}{col 15}{res}{space 2} .1193428{col 27}{space 2} .0112276{col 38}{space 1}  -22.60{col 47}{space 3}0.000{col 55}{space 4} .0992412{col 68}{space 3} .1435161
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit import_worsen ib3.age_3 $controls_demographics if infullmodel_TableD3 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      4.00
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                       import_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}age_3group {c |}
{space 30}18-29  {c |}{col 38}{res}{space 2} .9212805{col 50}{space 2} .1838501{col 61}{space 1}   -0.41{col 70}{space 3}0.681{col 78}{space 4}  .622981{col 91}{space 3} 1.362414
{txt}{space 30}30-49  {c |}{col 38}{res}{space 2} 1.043649{col 50}{space 2} .1385507{col 61}{space 1}    0.32{col 70}{space 3}0.748{col 78}{space 4} .8044847{col 91}{space 3} 1.353914
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6543863{col 50}{space 2} .0837702{col 61}{space 1}   -3.31{col 70}{space 3}0.001{col 78}{space 4} .5091382{col 91}{space 3} .8410709
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.113073{col 50}{space 2} .1462693{col 61}{space 1}    0.82{col 70}{space 3}0.415{col 78}{space 4} .8602666{col 91}{space 3} 1.440173
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .8912295{col 50}{space 2} .1472665{col 61}{space 1}   -0.70{col 70}{space 3}0.486{col 78}{space 4} .6446058{col 91}{space 3}  1.23221
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .5988927{col 50}{space 2} .0880808{col 61}{space 1}   -3.49{col 70}{space 3}0.000{col 78}{space 4} .4488717{col 91}{space 3} .7990534
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .8414512{col 50}{space 2}  .223756{col 61}{space 1}   -0.65{col 70}{space 3}0.516{col 78}{space 4} .4995868{col 91}{space 3} 1.417251
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.056759{col 50}{space 2} .2076127{col 61}{space 1}    0.28{col 70}{space 3}0.779{col 78}{space 4}  .718946{col 91}{space 3} 1.553301
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5350543{col 50}{space 2} .2184544{col 61}{space 1}   -1.53{col 70}{space 3}0.126{col 78}{space 4} .2403031{col 91}{space 3} 1.191342
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}  .879634{col 50}{space 2} .1907448{col 61}{space 1}   -0.59{col 70}{space 3}0.554{col 78}{space 4} .5749979{col 91}{space 3} 1.345668
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .6385776{col 50}{space 2} .1376237{col 61}{space 1}   -2.08{col 70}{space 3}0.037{col 78}{space 4} .4185141{col 91}{space 3} .9743552
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .7576182{col 50}{space 2} .1553592{col 61}{space 1}   -1.35{col 70}{space 3}0.176{col 78}{space 4} .5068127{col 91}{space 3}  1.13254
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .5546876{col 50}{space 2} .1239106{col 61}{space 1}   -2.64{col 70}{space 3}0.008{col 78}{space 4} .3579663{col 91}{space 3} .8595177
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.255098{col 50}{space 2} .2202454{col 61}{space 1}    1.29{col 70}{space 3}0.195{col 78}{space 4} .8897394{col 91}{space 3} 1.770487
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.174341{col 50}{space 2} .2245627{col 61}{space 1}    0.84{col 70}{space 3}0.401{col 78}{space 4} .8071867{col 91}{space 3} 1.708498
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.295484{col 50}{space 2} .2566914{col 61}{space 1}    1.31{col 70}{space 3}0.191{col 78}{space 4} .8784575{col 91}{space 3} 1.910484
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.729091{col 50}{space 2} .3422669{col 61}{space 1}    2.77{col 70}{space 3}0.006{col 78}{space 4} 1.172936{col 91}{space 3} 2.548951
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .1780432{col 50}{space 2} .0429683{col 61}{space 1}   -7.15{col 70}{space 3}0.000{col 78}{space 4} .1109269{col 91}{space 3} .2857684
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit import_worsen ib3.age_3 $controls_demographics  $controls_objective if infullmodel_TableD3 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      3.43
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                       import_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}age_3group {c |}
{space 30}18-29  {c |}{col 38}{res}{space 2} .9165761{col 50}{space 2} .1848456{col 61}{space 1}   -0.43{col 70}{space 3}0.666{col 78}{space 4} .6172406{col 91}{space 3} 1.361077
{txt}{space 30}30-49  {c |}{col 38}{res}{space 2} 1.049379{col 50}{space 2} .1398627{col 61}{space 1}    0.36{col 70}{space 3}0.718{col 78}{space 4} .8080688{col 91}{space 3}  1.36275
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}  .652949{col 50}{space 2} .0838384{col 61}{space 1}   -3.32{col 70}{space 3}0.001{col 78}{space 4} .5076354{col 91}{space 3} .8398595
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.106137{col 50}{space 2} .1468444{col 61}{space 1}    0.76{col 70}{space 3}0.447{col 78}{space 4}  .852656{col 91}{space 3} 1.434974
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .8677224{col 50}{space 2} .1436729{col 61}{space 1}   -0.86{col 70}{space 3}0.392{col 78}{space 4} .6271918{col 91}{space 3} 1.200498
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .5892368{col 50}{space 2} .0900676{col 61}{space 1}   -3.46{col 70}{space 3}0.001{col 78}{space 4} .4366566{col 91}{space 3} .7951329
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}  .849199{col 50}{space 2} .2281017{col 61}{space 1}   -0.61{col 70}{space 3}0.543{col 78}{space 4} .5015336{col 91}{space 3} 1.437868
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.072952{col 50}{space 2} .2137632{col 61}{space 1}    0.35{col 70}{space 3}0.724{col 78}{space 4} .7260127{col 91}{space 3} 1.585682
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5238108{col 50}{space 2} .2125478{col 61}{space 1}   -1.59{col 70}{space 3}0.111{col 78}{space 4}  .236415{col 91}{space 3} 1.160577
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .8804051{col 50}{space 2} .1915187{col 61}{space 1}   -0.59{col 70}{space 3}0.558{col 78}{space 4} .5747249{col 91}{space 3} 1.348668
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .6408041{col 50}{space 2} .1391445{col 61}{space 1}   -2.05{col 70}{space 3}0.040{col 78}{space 4} .4186379{col 91}{space 3} .9808713
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .7738245{col 50}{space 2} .1608112{col 61}{space 1}   -1.23{col 70}{space 3}0.217{col 78}{space 4} .5148696{col 91}{space 3} 1.163022
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .5645058{col 50}{space 2} .1274512{col 61}{space 1}   -2.53{col 70}{space 3}0.011{col 78}{space 4} .3626017{col 91}{space 3} .8788345
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.240085{col 50}{space 2} .2334789{col 61}{space 1}    1.14{col 70}{space 3}0.253{col 78}{space 4} .8573168{col 91}{space 3} 1.793749
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.238847{col 50}{space 2} .3908825{col 61}{space 1}    0.68{col 70}{space 3}0.497{col 78}{space 4} .6673647{col 91}{space 3} 2.299703
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.287317{col 50}{space 2} .2907146{col 61}{space 1}    1.12{col 70}{space 3}0.263{col 78}{space 4} .8267987{col 91}{space 3} 2.004338
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 2.010093{col 50}{space 2} .5449394{col 61}{space 1}    2.58{col 70}{space 3}0.010{col 78}{space 4} 1.181364{col 91}{space 3} 3.420178
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .7500211{col 50}{space 2} .1116783{col 61}{space 1}   -1.93{col 70}{space 3}0.053{col 78}{space 4} .5601323{col 91}{space 3} 1.004284
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 2.376303{col 50}{space 2} .7231256{col 61}{space 1}    2.84{col 70}{space 3}0.004{col 78}{space 4} 1.308567{col 91}{space 3} 4.315265
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 2.171363{col 50}{space 2} .6616116{col 61}{space 1}    2.54{col 70}{space 3}0.011{col 78}{space 4} 1.194794{col 91}{space 3} 3.946133
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .8999696{col 50}{space 2} .3310424{col 61}{space 1}   -0.29{col 70}{space 3}0.774{col 78}{space 4} .4375508{col 91}{space 3} 1.851088
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.086634{col 50}{space 2} .2196671{col 61}{space 1}    0.41{col 70}{space 3}0.681{col 78}{space 4}  .731067{col 91}{space 3} 1.615137
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} .9521414{col 50}{space 2} .1698551{col 61}{space 1}   -0.27{col 70}{space 3}0.783{col 78}{space 4} .6711304{col 91}{space 3} 1.350815
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .1067525{col 50}{space 2}  .035485{col 61}{space 1}   -6.73{col 70}{space 3}0.000{col 78}{space 4} .0556351{col 91}{space 3} .2048365
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 2: Improve 
. svy: logit import_improve ib3.age_3 if infullmodel_TableD3 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   2{txt},{res}   3961{txt}){col 67}= {res}     17.28
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}  Linearized
{col 1}import_improve{col 16}{c |} Odds Ratio{col 28}   Std. Err.{col 40}      t{col 48}   P>|t|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}age_3group {c |}
{space 8}18-29  {c |}{col 16}{res}{space 2}  4.80744{col 28}{space 2} 1.287311{col 39}{space 1}    5.86{col 48}{space 3}0.000{col 56}{space 4} 2.843897{col 69}{space 3} 8.126694
{txt}{space 8}30-49  {c |}{col 16}{res}{space 2} 2.398175{col 28}{space 2}  .581048{col 39}{space 1}    3.61{col 48}{space 3}0.000{col 56}{space 4}  1.49136{col 69}{space 3} 3.856377
{txt}{space 14} {c |}
{space 9}_cons {c |}{col 16}{res}{space 2} .0197312{col 28}{space 2} .0041182{col 39}{space 1}  -18.81{col 48}{space 3}0.000{col 56}{space 4} .0131051{col 69}{space 3} .0297076
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit import_improve ib3.age_3 $controls_demographics if infullmodel_TableD3 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      3.53
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                      import_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}age_3group {c |}
{space 30}18-29  {c |}{col 38}{res}{space 2} 4.855857{col 50}{space 2} 1.365446{col 61}{space 1}    5.62{col 70}{space 3}0.000{col 78}{space 4} 2.797941{col 91}{space 3} 8.427393
{txt}{space 30}30-49  {c |}{col 38}{res}{space 2} 2.337469{col 50}{space 2} .5839749{col 61}{space 1}    3.40{col 70}{space 3}0.001{col 78}{space 4} 1.432265{col 91}{space 3}  3.81477
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}  .819794{col 50}{space 2}  .148332{col 61}{space 1}   -1.10{col 70}{space 3}0.272{col 78}{space 4} .5749669{col 91}{space 3} 1.168871
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.022786{col 50}{space 2} .2006774{col 61}{space 1}    0.11{col 70}{space 3}0.909{col 78}{space 4} .6961809{col 91}{space 3} 1.502613
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .5329506{col 50}{space 2} .1449172{col 61}{space 1}   -2.31{col 70}{space 3}0.021{col 78}{space 4} .3127245{col 91}{space 3} .9082639
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .7019361{col 50}{space 2} .1437957{col 61}{space 1}   -1.73{col 70}{space 3}0.084{col 78}{space 4} .4697542{col 91}{space 3} 1.048877
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9356342{col 50}{space 2} .3569388{col 61}{space 1}   -0.17{col 70}{space 3}0.862{col 78}{space 4} .4428725{col 91}{space 3} 1.976667
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.143594{col 50}{space 2} .4221313{col 61}{space 1}    0.36{col 70}{space 3}0.716{col 78}{space 4} .5545935{col 91}{space 3} 2.358138
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.157162{col 50}{space 2} .5955343{col 61}{space 1}    0.28{col 70}{space 3}0.777{col 78}{space 4} .4218802{col 91}{space 3} 3.173946
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .3727353{col 50}{space 2}  .115085{col 61}{space 1}   -3.20{col 70}{space 3}0.001{col 78}{space 4} .2034721{col 91}{space 3}  .682804
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .3385937{col 50}{space 2}  .109652{col 61}{space 1}   -3.34{col 70}{space 3}0.001{col 78}{space 4} .1794474{col 91}{space 3}  .638882
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .4079394{col 50}{space 2} .1267116{col 61}{space 1}   -2.89{col 70}{space 3}0.004{col 78}{space 4}  .221881{col 91}{space 3} .7500174
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .2649332{col 50}{space 2} .0865925{col 61}{space 1}   -4.06{col 70}{space 3}0.000{col 78}{space 4} .1395851{col 91}{space 3} .5028447
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.498316{col 50}{space 2} .3613864{col 61}{space 1}    1.68{col 70}{space 3}0.094{col 78}{space 4} .9337597{col 91}{space 3} 2.404207
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}  .718643{col 50}{space 2} .2206643{col 61}{space 1}   -1.08{col 70}{space 3}0.282{col 78}{space 4} .3936099{col 91}{space 3}  1.31208
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .7920011{col 50}{space 2}  .249908{col 61}{space 1}   -0.74{col 70}{space 3}0.460{col 78}{space 4} .4266342{col 91}{space 3} 1.470266
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .9644461{col 50}{space 2} .3338255{col 61}{space 1}   -0.10{col 70}{space 3}0.917{col 78}{space 4} .4892828{col 91}{space 3} 1.901061
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .0667451{col 50}{space 2} .0260266{col 61}{space 1}   -6.94{col 70}{space 3}0.000{col 78}{space 4} .0310742{col 91}{space 3} .1433634
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit import_improve ib3.age_3 $controls_demographics $controls_objective if infullmodel_TableD3 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      2.81
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                      import_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}age_3group {c |}
{space 30}18-29  {c |}{col 38}{res}{space 2} 4.883435{col 50}{space 2} 1.383433{col 61}{space 1}    5.60{col 70}{space 3}0.000{col 78}{space 4} 2.802296{col 91}{space 3} 8.510142
{txt}{space 30}30-49  {c |}{col 38}{res}{space 2} 2.373198{col 50}{space 2} .5969493{col 61}{space 1}    3.44{col 70}{space 3}0.001{col 78}{space 4} 1.449303{col 91}{space 3} 3.886055
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .8222797{col 50}{space 2} .1492892{col 61}{space 1}   -1.08{col 70}{space 3}0.281{col 78}{space 4} .5760129{col 91}{space 3} 1.173835
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.031034{col 50}{space 2} .2059519{col 61}{space 1}    0.15{col 70}{space 3}0.878{col 78}{space 4} .6969337{col 91}{space 3} 1.525299
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2}  .532034{col 50}{space 2} .1453596{col 61}{space 1}   -2.31{col 70}{space 3}0.021{col 78}{space 4}  .311392{col 91}{space 3} .9090155
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .6794348{col 50}{space 2} .1425353{col 61}{space 1}   -1.84{col 70}{space 3}0.066{col 78}{space 4} .4503227{col 91}{space 3} 1.025113
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9105414{col 50}{space 2} .3478142{col 61}{space 1}   -0.25{col 70}{space 3}0.806{col 78}{space 4} .4305793{col 91}{space 3} 1.925512
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.146478{col 50}{space 2} .4223236{col 61}{space 1}    0.37{col 70}{space 3}0.711{col 78}{space 4} .5568215{col 91}{space 3} 2.360561
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.159751{col 50}{space 2} .5901265{col 61}{space 1}    0.29{col 70}{space 3}0.771{col 78}{space 4} .4276692{col 91}{space 3} 3.145007
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .3777598{col 50}{space 2} .1154978{col 61}{space 1}   -3.18{col 70}{space 3}0.001{col 78}{space 4} .2074371{col 91}{space 3} .6879312
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .3441002{col 50}{space 2} .1114877{col 61}{space 1}   -3.29{col 70}{space 3}0.001{col 78}{space 4} .1823111{col 91}{space 3} .6494664
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .4077277{col 50}{space 2} .1272669{col 61}{space 1}   -2.87{col 70}{space 3}0.004{col 78}{space 4} .2211045{col 91}{space 3} .7518703
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .2705826{col 50}{space 2} .0887859{col 61}{space 1}   -3.98{col 70}{space 3}0.000{col 78}{space 4} .1422037{col 91}{space 3} .5148598
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.453146{col 50}{space 2} .4086095{col 61}{space 1}    1.33{col 70}{space 3}0.184{col 78}{space 4} .8373113{col 91}{space 3}  2.52192
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.159795{col 50}{space 2}  .515801{col 61}{space 1}    0.33{col 70}{space 3}0.739{col 78}{space 4} .4849612{col 91}{space 3} 2.773676
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .7782851{col 50}{space 2} .3033448{col 61}{space 1}   -0.64{col 70}{space 3}0.520{col 78}{space 4} .3624704{col 91}{space 3} 1.671109
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .9747707{col 50}{space 2} .5281169{col 61}{space 1}   -0.05{col 70}{space 3}0.962{col 78}{space 4} .3369711{col 91}{space 3} 2.819761
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .9232136{col 50}{space 2} .2310483{col 61}{space 1}   -0.32{col 70}{space 3}0.750{col 78}{space 4} .5652115{col 91}{space 3} 1.507973
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} .9426035{col 50}{space 2} .4463135{col 61}{space 1}   -0.12{col 70}{space 3}0.901{col 78}{space 4} .3725377{col 91}{space 3} 2.384997
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.144866{col 50}{space 2} .5556917{col 61}{space 1}    0.28{col 70}{space 3}0.780{col 78}{space 4} .4420531{col 91}{space 3} 2.965068
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .5611062{col 50}{space 2} .3015494{col 61}{space 1}   -1.08{col 70}{space 3}0.282{col 78}{space 4} .1956379{col 91}{space 3} 1.609301
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.090376{col 50}{space 2} .3555053{col 61}{space 1}    0.27{col 70}{space 3}0.791{col 78}{space 4} .5753957{col 91}{space 3} 2.066266
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2}  1.36281{col 50}{space 2} .3875331{col 61}{space 1}    1.09{col 70}{space 3}0.276{col 78}{space 4} .7803887{col 91}{space 3} 2.379906
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .0592125{col 50}{space 2}  .030765{col 61}{space 1}   -5.44{col 70}{space 3}0.000{col 78}{space 4} .0213806{col 91}{space 3} .1639862
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 3: Worsen v Improve
. svy: logit import_directionworse ib3.age_3 if infullmodel_TableD3 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       533
{txt}{col 1}Number of PSUs{col 20}= {res}      533{txt}{col 49}Population size{col 67}={res} 592.486062
{txt}{col 49}Design df{col 67}= {res}       532
{txt}{col 49}F({res}   2{txt},{res}    531{txt}){col 67}= {res}     13.67
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}  Linearized
{col 1}import_directionworse{col 23}{c |} Odds Ratio{col 35}   Std. Err.{col 47}      t{col 55}   P>|t|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}age_3group {c |}
{space 15}18-29  {c |}{col 23}{res}{space 2} .1898874{col 35}{space 2} .0603348{col 46}{space 1}   -5.23{col 55}{space 3}0.000{col 63}{space 4} .1017229{col 76}{space 3} .3544651
{txt}{space 15}30-49  {c |}{col 23}{res}{space 2} .4128638{col 35}{space 2} .1105831{col 46}{space 1}   -3.30{col 55}{space 3}0.001{col 63}{space 4} .2439482{col 76}{space 3} .6987406
{txt}{space 21} {c |}
{space 16}_cons {c |}{col 23}{res}{space 2} 5.510176{col 35}{space 2} 1.240674{col 46}{space 1}    7.58{col 55}{space 3}0.000{col 63}{space 4} 3.540556{col 76}{space 3} 8.575501
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit import_directionworse ib3.age_3 $controls_demographics if infullmodel_TableD3 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       533
{txt}{col 1}Number of PSUs{col 20}= {res}      533{txt}{col 49}Population size{col 67}={res} 592.486062
{txt}{col 49}Design df{col 67}= {res}       532
{txt}{col 49}F({res}  17{txt},{res}    516{txt}){col 67}= {res}      2.28
{txt}{col 49}Prob > F{col 67}= {res}    0.0026

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}               import_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}age_3group {c |}
{space 30}18-29  {c |}{col 38}{res}{space 2} .2182825{col 50}{space 2} .0721483{col 61}{space 1}   -4.60{col 70}{space 3}0.000{col 78}{space 4} .1140335{col 91}{space 3} .4178358
{txt}{space 30}30-49  {c |}{col 38}{res}{space 2} .4676258{col 50}{space 2} .1320876{col 61}{space 1}   -2.69{col 70}{space 3}0.007{col 78}{space 4} .2684826{col 91}{space 3} .8144808
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .7548422{col 50}{space 2} .1706943{col 61}{space 1}   -1.24{col 70}{space 3}0.214{col 78}{space 4} .4840977{col 91}{space 3} 1.177008
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.101412{col 50}{space 2} .2589913{col 61}{space 1}    0.41{col 70}{space 3}0.681{col 78}{space 4} .6939658{col 91}{space 3} 1.748082
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.737503{col 50}{space 2}  .510699{col 61}{space 1}    1.88{col 70}{space 3}0.061{col 78}{space 4} .9753579{col 91}{space 3} 3.095189
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9391509{col 50}{space 2} .2415127{col 61}{space 1}   -0.24{col 70}{space 3}0.807{col 78}{space 4} .5666837{col 91}{space 3} 1.556432
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .8300581{col 50}{space 2} .3940198{col 61}{space 1}   -0.39{col 70}{space 3}0.695{col 78}{space 4} .3266862{col 91}{space 3} 2.109047
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.013492{col 50}{space 2} .4191468{col 61}{space 1}    0.03{col 70}{space 3}0.974{col 78}{space 4} .4497681{col 91}{space 3} 2.283767
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2}  .549621{col 50}{space 2} .3468396{col 61}{space 1}   -0.95{col 70}{space 3}0.343{col 78}{space 4} .1591057{col 91}{space 3} 1.898632
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.887767{col 50}{space 2} .6886964{col 61}{space 1}    1.74{col 70}{space 3}0.082{col 78}{space 4} .9219434{col 91}{space 3} 3.865381
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.645772{col 50}{space 2} .6414268{col 61}{space 1}    1.28{col 70}{space 3}0.202{col 78}{space 4}  .765357{col 91}{space 3} 3.538957
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.596431{col 50}{space 2} .5581033{col 61}{space 1}    1.34{col 70}{space 3}0.181{col 78}{space 4} .8033346{col 91}{space 3} 3.172517
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.797841{col 50}{space 2} .6174862{col 61}{space 1}    1.71{col 70}{space 3}0.088{col 78}{space 4}  .915653{col 91}{space 3} 3.529973
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .9201556{col 50}{space 2} .2876195{col 61}{space 1}   -0.27{col 70}{space 3}0.790{col 78}{space 4} .4979533{col 91}{space 3} 1.700333
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.676232{col 50}{space 2} .6010057{col 61}{space 1}    1.44{col 70}{space 3}0.150{col 78}{space 4} .8287885{col 91}{space 3} 3.390195
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.546733{col 50}{space 2} .5325191{col 61}{space 1}    1.27{col 70}{space 3}0.206{col 78}{space 4}  .786484{col 91}{space 3}  3.04187
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.556283{col 50}{space 2} .5677038{col 61}{space 1}    1.21{col 70}{space 3}0.226{col 78}{space 4} .7601124{col 91}{space 3} 3.186394
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 2.654105{col 50}{space 2} 1.108394{col 61}{space 1}    2.34{col 70}{space 3}0.020{col 78}{space 4} 1.168511{col 91}{space 3} 6.028415
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit import_directionworse ib3.age_3 $controls_demographics $controls_objective if infullmodel_TableD3 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       533
{txt}{col 1}Number of PSUs{col 20}= {res}      533{txt}{col 49}Population size{col 67}={res} 592.486062
{txt}{col 49}Design df{col 67}= {res}       532
{txt}{col 49}F({res}  23{txt},{res}    510{txt}){col 67}= {res}      1.76
{txt}{col 49}Prob > F{col 67}= {res}    0.0165

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}               import_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}age_3group {c |}
{space 30}18-29  {c |}{col 38}{res}{space 2}  .211234{col 50}{space 2} .0718574{col 61}{space 1}   -4.57{col 70}{space 3}0.000{col 78}{space 4} .1082786{col 91}{space 3} .4120835
{txt}{space 30}30-49  {c |}{col 38}{res}{space 2} .4886167{col 50}{space 2} .1405147{col 61}{space 1}   -2.49{col 70}{space 3}0.013{col 78}{space 4} .2777311{col 91}{space 3}  .859631
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .7399686{col 50}{space 2} .1671828{col 61}{space 1}   -1.33{col 70}{space 3}0.183{col 78}{space 4} .4747455{col 91}{space 3} 1.153362
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.080401{col 50}{space 2} .2550306{col 61}{space 1}    0.33{col 70}{space 3}0.743{col 78}{space 4} .6795148{col 91}{space 3} 1.717793
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.744612{col 50}{space 2} .5307557{col 61}{space 1}    1.83{col 70}{space 3}0.068{col 78}{space 4} .9597347{col 91}{space 3} 3.171368
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9074859{col 50}{space 2} .2415928{col 61}{space 1}   -0.36{col 70}{space 3}0.716{col 78}{space 4}  .537916{col 91}{space 3} 1.530965
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .7555217{col 50}{space 2} .3661597{col 61}{space 1}   -0.58{col 70}{space 3}0.563{col 78}{space 4} .2915922{col 91}{space 3} 1.957573
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2}   1.0286{col 50}{space 2} .4404442{col 61}{space 1}    0.07{col 70}{space 3}0.948{col 78}{space 4} .4435402{col 91}{space 3} 2.385395
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5997226{col 50}{space 2} .3572605{col 61}{space 1}   -0.86{col 70}{space 3}0.391{col 78}{space 4}  .186091{col 91}{space 3} 1.932749
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.779749{col 50}{space 2} .6540373{col 61}{space 1}    1.57{col 70}{space 3}0.117{col 78}{space 4} .8646471{col 91}{space 3} 3.663354
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.501792{col 50}{space 2} .5975836{col 61}{space 1}    1.02{col 70}{space 3}0.307{col 78}{space 4} .6872787{col 91}{space 3} 3.281609
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.504137{col 50}{space 2} .5283807{col 61}{space 1}    1.16{col 70}{space 3}0.246{col 78}{space 4} .7543821{col 91}{space 3} 2.999049
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.719397{col 50}{space 2} .5994877{col 61}{space 1}    1.55{col 70}{space 3}0.121{col 78}{space 4} .8667983{col 91}{space 3} 3.410626
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.015425{col 50}{space 2} .3558767{col 61}{space 1}    0.04{col 70}{space 3}0.965{col 78}{space 4} .5100897{col 91}{space 3} 2.021386
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.325183{col 50}{space 2} .6481626{col 61}{space 1}    0.58{col 70}{space 3}0.565{col 78}{space 4} .5069834{col 91}{space 3} 3.463841
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.595465{col 50}{space 2} .7151305{col 61}{space 1}    1.04{col 70}{space 3}0.298{col 78}{space 4} .6614326{col 91}{space 3} 3.848476
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.431199{col 50}{space 2} .8129854{col 61}{space 1}    0.63{col 70}{space 3}0.528{col 78}{space 4} .4688963{col 91}{space 3} 4.368409
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .8610504{col 50}{space 2}  .276007{col 61}{space 1}   -0.47{col 70}{space 3}0.641{col 78}{space 4} .4587292{col 91}{space 3} 1.616221
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.740808{col 50}{space 2} .9973024{col 61}{space 1}    0.97{col 70}{space 3}0.334{col 78}{space 4} .5649221{col 91}{space 3}   5.3643
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.508786{col 50}{space 2} .8598458{col 61}{space 1}    0.72{col 70}{space 3}0.471{col 78}{space 4} .4925246{col 91}{space 3}  4.62197
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} 1.317195{col 50}{space 2} .7961797{col 61}{space 1}    0.46{col 70}{space 3}0.649{col 78}{space 4} .4017605{col 91}{space 3} 4.318499
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .9289508{col 50}{space 2} .3635688{col 61}{space 1}   -0.19{col 70}{space 3}0.851{col 78}{space 4} .4306193{col 91}{space 3} 2.003973
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} .6042082{col 50}{space 2} .2071826{col 61}{space 1}   -1.47{col 70}{space 3}0.142{col 78}{space 4} .3080666{col 91}{space 3} 1.185028
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 2.502431{col 50}{space 2} 1.417254{col 61}{space 1}    1.62{col 70}{space 3}0.106{col 78}{space 4} .8225913{col 91}{space 3} 7.612727
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. 
. ******* Gender ********
. 
. * Model 1: Worsen 
. svy: logit import_worsen ib1.female_dummy if infullmodel_TableD3 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   1{txt},{res}   3962{txt}){col 67}= {res}     19.98
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}  Linearized
{col 1}import_worsen{col 15}{c |} Odds Ratio{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}female_dummy {c |}
{space 8}Male  {c |}{col 15}{res}{space 2} 1.691211{col 27}{space 2} .1988013{col 38}{space 1}    4.47{col 47}{space 3}0.000{col 55}{space 4} 1.343102{col 68}{space 3} 2.129545
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} .0852494{col 27}{space 2} .0073131{col 38}{space 1}  -28.70{col 47}{space 3}0.000{col 55}{space 4} .0720525{col 68}{space 3} .1008634
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit import_worsen ib1.female_dummy $controls_demographics if infullmodel_TableD3 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      4.00
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                       import_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.528149{col 50}{space 2} .1956235{col 61}{space 1}    3.31{col 70}{space 3}0.001{col 78}{space 4}  1.18896{col 91}{space 3} 1.964103
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.132824{col 50}{space 2} .2138252{col 61}{space 1}    0.66{col 70}{space 3}0.509{col 78}{space 4} .7824304{col 91}{space 3} 1.640134
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.085446{col 50}{space 2} .2166108{col 61}{space 1}    0.41{col 70}{space 3}0.681{col 78}{space 4} .7339914{col 91}{space 3} 1.605185
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.113073{col 50}{space 2} .1462693{col 61}{space 1}    0.82{col 70}{space 3}0.415{col 78}{space 4} .8602666{col 91}{space 3} 1.440173
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .8912295{col 50}{space 2} .1472665{col 61}{space 1}   -0.70{col 70}{space 3}0.486{col 78}{space 4} .6446058{col 91}{space 3}  1.23221
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .5988927{col 50}{space 2} .0880808{col 61}{space 1}   -3.49{col 70}{space 3}0.000{col 78}{space 4} .4488717{col 91}{space 3} .7990534
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .8414512{col 50}{space 2}  .223756{col 61}{space 1}   -0.65{col 70}{space 3}0.516{col 78}{space 4} .4995868{col 91}{space 3} 1.417251
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.056759{col 50}{space 2} .2076127{col 61}{space 1}    0.28{col 70}{space 3}0.779{col 78}{space 4}  .718946{col 91}{space 3} 1.553301
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5350543{col 50}{space 2} .2184544{col 61}{space 1}   -1.53{col 70}{space 3}0.126{col 78}{space 4} .2403031{col 91}{space 3} 1.191342
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}  .879634{col 50}{space 2} .1907448{col 61}{space 1}   -0.59{col 70}{space 3}0.554{col 78}{space 4} .5749979{col 91}{space 3} 1.345668
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .6385776{col 50}{space 2} .1376237{col 61}{space 1}   -2.08{col 70}{space 3}0.037{col 78}{space 4} .4185141{col 91}{space 3} .9743552
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .7576182{col 50}{space 2} .1553592{col 61}{space 1}   -1.35{col 70}{space 3}0.176{col 78}{space 4} .5068127{col 91}{space 3}  1.13254
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .5546876{col 50}{space 2} .1239106{col 61}{space 1}   -2.64{col 70}{space 3}0.008{col 78}{space 4} .3579663{col 91}{space 3} .8595177
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.255098{col 50}{space 2} .2202454{col 61}{space 1}    1.29{col 70}{space 3}0.195{col 78}{space 4} .8897394{col 91}{space 3} 1.770487
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.174341{col 50}{space 2} .2245627{col 61}{space 1}    0.84{col 70}{space 3}0.401{col 78}{space 4} .8071867{col 91}{space 3} 1.708498
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.295484{col 50}{space 2} .2566914{col 61}{space 1}    1.31{col 70}{space 3}0.191{col 78}{space 4} .8784575{col 91}{space 3} 1.910484
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.729091{col 50}{space 2} .3422669{col 61}{space 1}    2.77{col 70}{space 3}0.006{col 78}{space 4} 1.172936{col 91}{space 3} 2.548951
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .1073375{col 50}{space 2} .0287175{col 61}{space 1}   -8.34{col 70}{space 3}0.000{col 78}{space 4} .0635256{col 91}{space 3} .1813655
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit import_worsen ib1.female_dummy $controls_demographics  $controls_objective if infullmodel_TableD3 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      3.43
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                       import_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.531513{col 50}{space 2} .1966457{col 61}{space 1}    3.32{col 70}{space 3}0.001{col 78}{space 4} 1.190675{col 91}{space 3} 1.969918
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2}  1.14489{col 50}{space 2} .2193418{col 61}{space 1}    0.71{col 70}{space 3}0.480{col 78}{space 4} .7863899{col 91}{space 3} 1.666824
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.091017{col 50}{space 2} .2200251{col 61}{space 1}    0.43{col 70}{space 3}0.666{col 78}{space 4} .7347125{col 91}{space 3} 1.620114
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.106137{col 50}{space 2} .1468444{col 61}{space 1}    0.76{col 70}{space 3}0.447{col 78}{space 4}  .852656{col 91}{space 3} 1.434974
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .8677224{col 50}{space 2} .1436729{col 61}{space 1}   -0.86{col 70}{space 3}0.392{col 78}{space 4} .6271918{col 91}{space 3} 1.200498
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .5892368{col 50}{space 2} .0900676{col 61}{space 1}   -3.46{col 70}{space 3}0.001{col 78}{space 4} .4366566{col 91}{space 3} .7951329
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}  .849199{col 50}{space 2} .2281017{col 61}{space 1}   -0.61{col 70}{space 3}0.543{col 78}{space 4} .5015336{col 91}{space 3} 1.437868
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.072952{col 50}{space 2} .2137632{col 61}{space 1}    0.35{col 70}{space 3}0.724{col 78}{space 4} .7260127{col 91}{space 3} 1.585682
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5238108{col 50}{space 2} .2125478{col 61}{space 1}   -1.59{col 70}{space 3}0.111{col 78}{space 4}  .236415{col 91}{space 3} 1.160577
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .8804051{col 50}{space 2} .1915187{col 61}{space 1}   -0.59{col 70}{space 3}0.558{col 78}{space 4} .5747249{col 91}{space 3} 1.348668
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .6408041{col 50}{space 2} .1391445{col 61}{space 1}   -2.05{col 70}{space 3}0.040{col 78}{space 4} .4186379{col 91}{space 3} .9808713
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .7738245{col 50}{space 2} .1608112{col 61}{space 1}   -1.23{col 70}{space 3}0.217{col 78}{space 4} .5148696{col 91}{space 3} 1.163022
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .5645058{col 50}{space 2} .1274512{col 61}{space 1}   -2.53{col 70}{space 3}0.011{col 78}{space 4} .3626017{col 91}{space 3} .8788345
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.240085{col 50}{space 2} .2334789{col 61}{space 1}    1.14{col 70}{space 3}0.253{col 78}{space 4} .8573168{col 91}{space 3} 1.793749
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.238847{col 50}{space 2} .3908825{col 61}{space 1}    0.68{col 70}{space 3}0.497{col 78}{space 4} .6673647{col 91}{space 3} 2.299703
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.287317{col 50}{space 2} .2907146{col 61}{space 1}    1.12{col 70}{space 3}0.263{col 78}{space 4} .8267987{col 91}{space 3} 2.004338
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 2.010093{col 50}{space 2} .5449394{col 61}{space 1}    2.58{col 70}{space 3}0.010{col 78}{space 4} 1.181364{col 91}{space 3} 3.420178
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .7500211{col 50}{space 2} .1116783{col 61}{space 1}   -1.93{col 70}{space 3}0.053{col 78}{space 4} .5601323{col 91}{space 3} 1.004284
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 2.376303{col 50}{space 2} .7231256{col 61}{space 1}    2.84{col 70}{space 3}0.004{col 78}{space 4} 1.308567{col 91}{space 3} 4.315265
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 2.171363{col 50}{space 2} .6616116{col 61}{space 1}    2.54{col 70}{space 3}0.011{col 78}{space 4} 1.194794{col 91}{space 3} 3.946133
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .8999696{col 50}{space 2} .3310424{col 61}{space 1}   -0.29{col 70}{space 3}0.774{col 78}{space 4} .4375508{col 91}{space 3} 1.851088
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.086634{col 50}{space 2} .2196671{col 61}{space 1}    0.41{col 70}{space 3}0.681{col 78}{space 4}  .731067{col 91}{space 3} 1.615137
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} .9521414{col 50}{space 2} .1698551{col 61}{space 1}   -0.27{col 70}{space 3}0.783{col 78}{space 4} .6711304{col 91}{space 3} 1.350815
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2}  .063889{col 50}{space 2}  .023316{col 61}{space 1}   -7.54{col 70}{space 3}0.000{col 78}{space 4} .0312384{col 91}{space 3} .1306663
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 2: Improve 
. svy: logit import_improve ib1.female_dummy if infullmodel_TableD3 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   1{txt},{res}   3962{txt}){col 67}= {res}      2.55
{txt}{col 49}Prob > F{col 67}= {res}    0.1107

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}  Linearized
{col 1}import_improve{col 16}{c |} Odds Ratio{col 28}   Std. Err.{col 40}      t{col 48}   P>|t|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}female_dummy {c |}
{space 9}Male  {c |}{col 16}{res}{space 2} 1.324772{col 28}{space 2} .2335405{col 39}{space 1}    1.60{col 48}{space 3}0.111{col 56}{space 4} .9376464{col 69}{space 3} 1.871729
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .0383693{col 28}{space 2} .0046417{col 39}{space 1}  -26.95{col 48}{space 3}0.000{col 56}{space 4} .0302676{col 69}{space 3} .0486395
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit import_improve ib1.female_dummy $controls_demographics if infullmodel_TableD3 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      3.53
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                      import_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.219819{col 50}{space 2} .2207117{col 61}{space 1}    1.10{col 70}{space 3}0.272{col 78}{space 4} .8555263{col 91}{space 3} 1.739231
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .4813711{col 50}{space 2} .0989775{col 61}{space 1}   -3.56{col 70}{space 3}0.000{col 78}{space 4} .3216666{col 91}{space 3} .7203673
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .2059369{col 50}{space 2} .0579086{col 61}{space 1}   -5.62{col 70}{space 3}0.000{col 78}{space 4} .1186607{col 91}{space 3} .3574057
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.022786{col 50}{space 2} .2006774{col 61}{space 1}    0.11{col 70}{space 3}0.909{col 78}{space 4} .6961809{col 91}{space 3} 1.502613
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .5329506{col 50}{space 2} .1449172{col 61}{space 1}   -2.31{col 70}{space 3}0.021{col 78}{space 4} .3127245{col 91}{space 3} .9082639
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .7019361{col 50}{space 2} .1437957{col 61}{space 1}   -1.73{col 70}{space 3}0.084{col 78}{space 4} .4697542{col 91}{space 3} 1.048877
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9356342{col 50}{space 2} .3569388{col 61}{space 1}   -0.17{col 70}{space 3}0.862{col 78}{space 4} .4428725{col 91}{space 3} 1.976667
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.143594{col 50}{space 2} .4221313{col 61}{space 1}    0.36{col 70}{space 3}0.716{col 78}{space 4} .5545935{col 91}{space 3} 2.358138
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.157162{col 50}{space 2} .5955343{col 61}{space 1}    0.28{col 70}{space 3}0.777{col 78}{space 4} .4218802{col 91}{space 3} 3.173946
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .3727353{col 50}{space 2}  .115085{col 61}{space 1}   -3.20{col 70}{space 3}0.001{col 78}{space 4} .2034721{col 91}{space 3}  .682804
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .3385937{col 50}{space 2}  .109652{col 61}{space 1}   -3.34{col 70}{space 3}0.001{col 78}{space 4} .1794474{col 91}{space 3}  .638882
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .4079394{col 50}{space 2} .1267116{col 61}{space 1}   -2.89{col 70}{space 3}0.004{col 78}{space 4}  .221881{col 91}{space 3} .7500174
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .2649332{col 50}{space 2} .0865925{col 61}{space 1}   -4.06{col 70}{space 3}0.000{col 78}{space 4} .1395851{col 91}{space 3} .5028447
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.498316{col 50}{space 2} .3613864{col 61}{space 1}    1.68{col 70}{space 3}0.094{col 78}{space 4} .9337597{col 91}{space 3} 2.404207
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}  .718643{col 50}{space 2} .2206643{col 61}{space 1}   -1.08{col 70}{space 3}0.282{col 78}{space 4} .3936099{col 91}{space 3}  1.31208
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .7920011{col 50}{space 2}  .249908{col 61}{space 1}   -0.74{col 70}{space 3}0.460{col 78}{space 4} .4266342{col 91}{space 3} 1.470266
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .9644461{col 50}{space 2} .3338255{col 61}{space 1}   -0.10{col 70}{space 3}0.917{col 78}{space 4} .4892828{col 91}{space 3} 1.901061
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2656989{col 50}{space 2} .1001126{col 61}{space 1}   -3.52{col 70}{space 3}0.000{col 78}{space 4}  .126931{col 91}{space 3} .5561755
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit import_improve ib1.female_dummy $controls_demographics $controls_objective if infullmodel_TableD3 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      2.81
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                      import_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.216131{col 50}{space 2}  .220795{col 61}{space 1}    1.08{col 70}{space 3}0.281{col 78}{space 4} .8519087{col 91}{space 3} 1.736072
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .4859691{col 50}{space 2}  .100689{col 61}{space 1}   -3.48{col 70}{space 3}0.001{col 78}{space 4} .3237371{col 91}{space 3} .7294992
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .2047739{col 50}{space 2} .0580106{col 61}{space 1}   -5.60{col 70}{space 3}0.000{col 78}{space 4} .1175069{col 91}{space 3} .3568502
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.031034{col 50}{space 2} .2059519{col 61}{space 1}    0.15{col 70}{space 3}0.878{col 78}{space 4} .6969337{col 91}{space 3} 1.525299
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2}  .532034{col 50}{space 2} .1453596{col 61}{space 1}   -2.31{col 70}{space 3}0.021{col 78}{space 4}  .311392{col 91}{space 3} .9090155
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .6794348{col 50}{space 2} .1425353{col 61}{space 1}   -1.84{col 70}{space 3}0.066{col 78}{space 4} .4503227{col 91}{space 3} 1.025113
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9105414{col 50}{space 2} .3478142{col 61}{space 1}   -0.25{col 70}{space 3}0.806{col 78}{space 4} .4305793{col 91}{space 3} 1.925512
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.146478{col 50}{space 2} .4223236{col 61}{space 1}    0.37{col 70}{space 3}0.711{col 78}{space 4} .5568215{col 91}{space 3} 2.360561
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.159751{col 50}{space 2} .5901265{col 61}{space 1}    0.29{col 70}{space 3}0.771{col 78}{space 4} .4276692{col 91}{space 3} 3.145007
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .3777598{col 50}{space 2} .1154978{col 61}{space 1}   -3.18{col 70}{space 3}0.001{col 78}{space 4} .2074371{col 91}{space 3} .6879312
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .3441002{col 50}{space 2} .1114877{col 61}{space 1}   -3.29{col 70}{space 3}0.001{col 78}{space 4} .1823111{col 91}{space 3} .6494664
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .4077277{col 50}{space 2} .1272669{col 61}{space 1}   -2.87{col 70}{space 3}0.004{col 78}{space 4} .2211045{col 91}{space 3} .7518703
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .2705826{col 50}{space 2} .0887859{col 61}{space 1}   -3.98{col 70}{space 3}0.000{col 78}{space 4} .1422037{col 91}{space 3} .5148598
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.453146{col 50}{space 2} .4086095{col 61}{space 1}    1.33{col 70}{space 3}0.184{col 78}{space 4} .8373113{col 91}{space 3}  2.52192
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.159795{col 50}{space 2}  .515801{col 61}{space 1}    0.33{col 70}{space 3}0.739{col 78}{space 4} .4849612{col 91}{space 3} 2.773676
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .7782851{col 50}{space 2} .3033448{col 61}{space 1}   -0.64{col 70}{space 3}0.520{col 78}{space 4} .3624704{col 91}{space 3} 1.671109
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .9747707{col 50}{space 2} .5281169{col 61}{space 1}   -0.05{col 70}{space 3}0.962{col 78}{space 4} .3369711{col 91}{space 3} 2.819761
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .9232136{col 50}{space 2} .2310483{col 61}{space 1}   -0.32{col 70}{space 3}0.750{col 78}{space 4} .5652115{col 91}{space 3} 1.507973
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} .9426035{col 50}{space 2} .4463135{col 61}{space 1}   -0.12{col 70}{space 3}0.901{col 78}{space 4} .3725377{col 91}{space 3} 2.384997
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.144866{col 50}{space 2} .5556917{col 61}{space 1}    0.28{col 70}{space 3}0.780{col 78}{space 4} .4420531{col 91}{space 3} 2.965068
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .5611062{col 50}{space 2} .3015494{col 61}{space 1}   -1.08{col 70}{space 3}0.282{col 78}{space 4} .1956379{col 91}{space 3} 1.609301
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.090376{col 50}{space 2} .3555053{col 61}{space 1}    0.27{col 70}{space 3}0.791{col 78}{space 4} .5753957{col 91}{space 3} 2.066266
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2}  1.36281{col 50}{space 2} .3875331{col 61}{space 1}    1.09{col 70}{space 3}0.276{col 78}{space 4} .7803887{col 91}{space 3} 2.379906
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2377706{col 50}{space 2} .1221171{col 61}{space 1}   -2.80{col 70}{space 3}0.005{col 78}{space 4} .0868669{col 91}{space 3} .6508217
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 3: Worsen v Improve
. svy: logit import_directionworse ib1.female_dummy if infullmodel_TableD3 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       533
{txt}{col 1}Number of PSUs{col 20}= {res}      533{txt}{col 49}Population size{col 67}={res} 592.486062
{txt}{col 49}Design df{col 67}= {res}       532
{txt}{col 49}F({res}   1{txt},{res}    532{txt}){col 67}= {res}      0.98
{txt}{col 49}Prob > F{col 67}= {res}    0.3239

{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}  Linearized
{col 1}import_directionworse{col 23}{c |} Odds Ratio{col 35}   Std. Err.{col 47}      t{col 55}   P>|t|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}female_dummy {c |}
{space 16}Male  {c |}{col 23}{res}{space 2} 1.225392{col 35}{space 2} .2522425{col 46}{space 1}    0.99{col 55}{space 3}0.324{col 63}{space 4} .8178217{col 76}{space 3} 1.836079
{txt}{space 16}_cons {c |}{col 23}{res}{space 2} 2.125837{col 35}{space 2} .3074741{col 46}{space 1}    5.21{col 55}{space 3}0.000{col 63}{space 4} 1.600052{col 76}{space 3} 2.824396
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit import_directionworse ib1.female_dummy $controls_demographics if infullmodel_TableD3 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       533
{txt}{col 1}Number of PSUs{col 20}= {res}      533{txt}{col 49}Population size{col 67}={res} 592.486062
{txt}{col 49}Design df{col 67}= {res}       532
{txt}{col 49}F({res}  17{txt},{res}    516{txt}){col 67}= {res}      2.28
{txt}{col 49}Prob > F{col 67}= {res}    0.0026

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}               import_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2}  1.32478{col 50}{space 2} .2995758{col 61}{space 1}    1.24{col 70}{space 3}0.214{col 78}{space 4} .8496119{col 91}{space 3} 2.065699
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 2.142296{col 50}{space 2} .5710227{col 61}{space 1}    2.86{col 70}{space 3}0.004{col 78}{space 4} 1.269044{col 91}{space 3} 3.616447
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 4.581218{col 50}{space 2} 1.514217{col 61}{space 1}    4.60{col 70}{space 3}0.000{col 78}{space 4} 2.393284{col 91}{space 3} 8.769355
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.101412{col 50}{space 2} .2589913{col 61}{space 1}    0.41{col 70}{space 3}0.681{col 78}{space 4} .6939658{col 91}{space 3} 1.748082
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.737503{col 50}{space 2}  .510699{col 61}{space 1}    1.88{col 70}{space 3}0.061{col 78}{space 4} .9753579{col 91}{space 3} 3.095189
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9391509{col 50}{space 2} .2415127{col 61}{space 1}   -0.24{col 70}{space 3}0.807{col 78}{space 4} .5666837{col 91}{space 3} 1.556432
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .8300581{col 50}{space 2} .3940198{col 61}{space 1}   -0.39{col 70}{space 3}0.695{col 78}{space 4} .3266862{col 91}{space 3} 2.109047
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.013492{col 50}{space 2} .4191468{col 61}{space 1}    0.03{col 70}{space 3}0.974{col 78}{space 4} .4497681{col 91}{space 3} 2.283767
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2}  .549621{col 50}{space 2} .3468396{col 61}{space 1}   -0.95{col 70}{space 3}0.343{col 78}{space 4} .1591057{col 91}{space 3} 1.898632
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.887767{col 50}{space 2} .6886964{col 61}{space 1}    1.74{col 70}{space 3}0.082{col 78}{space 4} .9219434{col 91}{space 3} 3.865381
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.645772{col 50}{space 2} .6414268{col 61}{space 1}    1.28{col 70}{space 3}0.202{col 78}{space 4}  .765357{col 91}{space 3} 3.538957
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.596431{col 50}{space 2} .5581033{col 61}{space 1}    1.34{col 70}{space 3}0.181{col 78}{space 4} .8033346{col 91}{space 3} 3.172517
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.797841{col 50}{space 2} .6174862{col 61}{space 1}    1.71{col 70}{space 3}0.088{col 78}{space 4}  .915653{col 91}{space 3} 3.529973
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .9201556{col 50}{space 2} .2876195{col 61}{space 1}   -0.27{col 70}{space 3}0.790{col 78}{space 4} .4979533{col 91}{space 3} 1.700333
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.676232{col 50}{space 2} .6010057{col 61}{space 1}    1.44{col 70}{space 3}0.150{col 78}{space 4} .8287885{col 91}{space 3} 3.390195
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.546733{col 50}{space 2} .5325191{col 61}{space 1}    1.27{col 70}{space 3}0.206{col 78}{space 4}  .786484{col 91}{space 3}  3.04187
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.556283{col 50}{space 2} .5677038{col 61}{space 1}    1.21{col 70}{space 3}0.226{col 78}{space 4} .7601124{col 91}{space 3} 3.186394
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .4373139{col 50}{space 2} .1928199{col 61}{space 1}   -1.88{col 70}{space 3}0.061{col 78}{space 4}  .183919{col 91}{space 3} 1.039824
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit import_directionworse ib1.female_dummy $controls_demographics $controls_objective if infullmodel_TableD3 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       533
{txt}{col 1}Number of PSUs{col 20}= {res}      533{txt}{col 49}Population size{col 67}={res} 592.486062
{txt}{col 49}Design df{col 67}= {res}       532
{txt}{col 49}F({res}  23{txt},{res}    510{txt}){col 67}= {res}      1.76
{txt}{col 49}Prob > F{col 67}= {res}    0.0165

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}               import_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.351409{col 50}{space 2} .3053269{col 61}{space 1}    1.33{col 70}{space 3}0.183{col 78}{space 4} .8670303{col 91}{space 3} 2.106392
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 2.313153{col 50}{space 2} .6313656{col 61}{space 1}    3.07{col 70}{space 3}0.002{col 78}{space 4}  1.35314{col 91}{space 3} 3.954268
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 4.734086{col 50}{space 2} 1.610438{col 61}{space 1}    4.57{col 70}{space 3}0.000{col 78}{space 4} 2.426693{col 91}{space 3} 9.235438
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.080401{col 50}{space 2} .2550306{col 61}{space 1}    0.33{col 70}{space 3}0.743{col 78}{space 4} .6795148{col 91}{space 3} 1.717793
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.744612{col 50}{space 2} .5307557{col 61}{space 1}    1.83{col 70}{space 3}0.068{col 78}{space 4} .9597347{col 91}{space 3} 3.171368
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9074859{col 50}{space 2} .2415928{col 61}{space 1}   -0.36{col 70}{space 3}0.716{col 78}{space 4}  .537916{col 91}{space 3} 1.530965
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .7555217{col 50}{space 2} .3661597{col 61}{space 1}   -0.58{col 70}{space 3}0.563{col 78}{space 4} .2915922{col 91}{space 3} 1.957573
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2}   1.0286{col 50}{space 2} .4404442{col 61}{space 1}    0.07{col 70}{space 3}0.948{col 78}{space 4} .4435402{col 91}{space 3} 2.385395
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5997226{col 50}{space 2} .3572605{col 61}{space 1}   -0.86{col 70}{space 3}0.391{col 78}{space 4}  .186091{col 91}{space 3} 1.932749
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.779749{col 50}{space 2} .6540373{col 61}{space 1}    1.57{col 70}{space 3}0.117{col 78}{space 4} .8646471{col 91}{space 3} 3.663354
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.501792{col 50}{space 2} .5975836{col 61}{space 1}    1.02{col 70}{space 3}0.307{col 78}{space 4} .6872787{col 91}{space 3} 3.281609
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.504137{col 50}{space 2} .5283807{col 61}{space 1}    1.16{col 70}{space 3}0.246{col 78}{space 4} .7543821{col 91}{space 3} 2.999049
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.719397{col 50}{space 2} .5994877{col 61}{space 1}    1.55{col 70}{space 3}0.121{col 78}{space 4} .8667983{col 91}{space 3} 3.410626
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.015425{col 50}{space 2} .3558767{col 61}{space 1}    0.04{col 70}{space 3}0.965{col 78}{space 4} .5100897{col 91}{space 3} 2.021386
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.325183{col 50}{space 2} .6481626{col 61}{space 1}    0.58{col 70}{space 3}0.565{col 78}{space 4} .5069834{col 91}{space 3} 3.463841
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.595465{col 50}{space 2} .7151305{col 61}{space 1}    1.04{col 70}{space 3}0.298{col 78}{space 4} .6614326{col 91}{space 3} 3.848476
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.431199{col 50}{space 2} .8129854{col 61}{space 1}    0.63{col 70}{space 3}0.528{col 78}{space 4} .4688963{col 91}{space 3} 4.368409
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .8610504{col 50}{space 2}  .276007{col 61}{space 1}   -0.47{col 70}{space 3}0.641{col 78}{space 4} .4587292{col 91}{space 3} 1.616221
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.740808{col 50}{space 2} .9973024{col 61}{space 1}    0.97{col 70}{space 3}0.334{col 78}{space 4} .5649221{col 91}{space 3}   5.3643
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.508786{col 50}{space 2} .8598458{col 61}{space 1}    0.72{col 70}{space 3}0.471{col 78}{space 4} .4925246{col 91}{space 3}  4.62197
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} 1.317195{col 50}{space 2} .7961797{col 61}{space 1}    0.46{col 70}{space 3}0.649{col 78}{space 4} .4017605{col 91}{space 3} 4.318499
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .9289508{col 50}{space 2} .3635688{col 61}{space 1}   -0.19{col 70}{space 3}0.851{col 78}{space 4} .4306193{col 91}{space 3} 2.003973
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} .6042082{col 50}{space 2} .2071826{col 61}{space 1}   -1.47{col 70}{space 3}0.142{col 78}{space 4} .3080666{col 91}{space 3} 1.185028
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .3911464{col 50}{space 2} .2186024{col 61}{space 1}   -1.68{col 70}{space 3}0.094{col 78}{space 4} .1304783{col 91}{space 3} 1.172574
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. 
. ******* Education ********
. 
. * Model 1: Worsen 
. svy: logit import_worsen i.degree_dummy if infullmodel_TableD3 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   1{txt},{res}   3962{txt}){col 67}= {res}      2.18
{txt}{col 49}Prob > F{col 67}= {res}    0.1401

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}  Linearized
{col 1} import_worsen{col 16}{c |} Odds Ratio{col 28}   Std. Err.{col 40}      t{col 48}   P>|t|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}degree_dummy {c |}
Degree-Holder  {c |}{col 16}{res}{space 2} .8389396{col 28}{space 2} .0998482{col 39}{space 1}   -1.48{col 48}{space 3}0.140{col 56}{space 4} .6643426{col 69}{space 3} 1.059423
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .1230209{col 28}{space 2} .0096963{col 39}{space 1}  -26.59{col 48}{space 3}0.000{col 56}{space 4} .1054066{col 69}{space 3} .1435787
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit import_worsen i.degree_dummy $controls_demographics if infullmodel_TableD3 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      4.00
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                       import_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.113073{col 50}{space 2} .1462693{col 61}{space 1}    0.82{col 70}{space 3}0.415{col 78}{space 4} .8602666{col 91}{space 3} 1.440173
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.132824{col 50}{space 2} .2138252{col 61}{space 1}    0.66{col 70}{space 3}0.509{col 78}{space 4} .7824304{col 91}{space 3} 1.640134
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.085446{col 50}{space 2} .2166108{col 61}{space 1}    0.41{col 70}{space 3}0.681{col 78}{space 4} .7339914{col 91}{space 3} 1.605185
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6543863{col 50}{space 2} .0837702{col 61}{space 1}   -3.31{col 70}{space 3}0.001{col 78}{space 4} .5091382{col 91}{space 3} .8410709
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .8912295{col 50}{space 2} .1472665{col 61}{space 1}   -0.70{col 70}{space 3}0.486{col 78}{space 4} .6446058{col 91}{space 3}  1.23221
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .5988927{col 50}{space 2} .0880808{col 61}{space 1}   -3.49{col 70}{space 3}0.000{col 78}{space 4} .4488717{col 91}{space 3} .7990534
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .8414512{col 50}{space 2}  .223756{col 61}{space 1}   -0.65{col 70}{space 3}0.516{col 78}{space 4} .4995868{col 91}{space 3} 1.417251
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.056759{col 50}{space 2} .2076127{col 61}{space 1}    0.28{col 70}{space 3}0.779{col 78}{space 4}  .718946{col 91}{space 3} 1.553301
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5350543{col 50}{space 2} .2184544{col 61}{space 1}   -1.53{col 70}{space 3}0.126{col 78}{space 4} .2403031{col 91}{space 3} 1.191342
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}  .879634{col 50}{space 2} .1907448{col 61}{space 1}   -0.59{col 70}{space 3}0.554{col 78}{space 4} .5749979{col 91}{space 3} 1.345668
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .6385776{col 50}{space 2} .1376237{col 61}{space 1}   -2.08{col 70}{space 3}0.037{col 78}{space 4} .4185141{col 91}{space 3} .9743552
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .7576182{col 50}{space 2} .1553592{col 61}{space 1}   -1.35{col 70}{space 3}0.176{col 78}{space 4} .5068127{col 91}{space 3}  1.13254
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .5546876{col 50}{space 2} .1239106{col 61}{space 1}   -2.64{col 70}{space 3}0.008{col 78}{space 4} .3579663{col 91}{space 3} .8595177
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.255098{col 50}{space 2} .2202454{col 61}{space 1}    1.29{col 70}{space 3}0.195{col 78}{space 4} .8897394{col 91}{space 3} 1.770487
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.174341{col 50}{space 2} .2245627{col 61}{space 1}    0.84{col 70}{space 3}0.401{col 78}{space 4} .8071867{col 91}{space 3} 1.708498
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.295484{col 50}{space 2} .2566914{col 61}{space 1}    1.31{col 70}{space 3}0.191{col 78}{space 4} .8784575{col 91}{space 3} 1.910484
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.729091{col 50}{space 2} .3422669{col 61}{space 1}    2.77{col 70}{space 3}0.006{col 78}{space 4} 1.172936{col 91}{space 3} 2.548951
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .1640278{col 50}{space 2} .0453379{col 61}{space 1}   -6.54{col 70}{space 3}0.000{col 78}{space 4} .0954048{col 91}{space 3}   .28201
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit import_worsen i.degree_dummy $controls_demographics  $controls_objective if infullmodel_TableD3 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      3.43
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                       import_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.106137{col 50}{space 2} .1468444{col 61}{space 1}    0.76{col 70}{space 3}0.447{col 78}{space 4}  .852656{col 91}{space 3} 1.434974
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2}  1.14489{col 50}{space 2} .2193418{col 61}{space 1}    0.71{col 70}{space 3}0.480{col 78}{space 4} .7863899{col 91}{space 3} 1.666824
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.091017{col 50}{space 2} .2200251{col 61}{space 1}    0.43{col 70}{space 3}0.666{col 78}{space 4} .7347125{col 91}{space 3} 1.620114
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}  .652949{col 50}{space 2} .0838384{col 61}{space 1}   -3.32{col 70}{space 3}0.001{col 78}{space 4} .5076354{col 91}{space 3} .8398595
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .8677224{col 50}{space 2} .1436729{col 61}{space 1}   -0.86{col 70}{space 3}0.392{col 78}{space 4} .6271918{col 91}{space 3} 1.200498
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .5892368{col 50}{space 2} .0900676{col 61}{space 1}   -3.46{col 70}{space 3}0.001{col 78}{space 4} .4366566{col 91}{space 3} .7951329
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}  .849199{col 50}{space 2} .2281017{col 61}{space 1}   -0.61{col 70}{space 3}0.543{col 78}{space 4} .5015336{col 91}{space 3} 1.437868
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.072952{col 50}{space 2} .2137632{col 61}{space 1}    0.35{col 70}{space 3}0.724{col 78}{space 4} .7260127{col 91}{space 3} 1.585682
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5238108{col 50}{space 2} .2125478{col 61}{space 1}   -1.59{col 70}{space 3}0.111{col 78}{space 4}  .236415{col 91}{space 3} 1.160577
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .8804051{col 50}{space 2} .1915187{col 61}{space 1}   -0.59{col 70}{space 3}0.558{col 78}{space 4} .5747249{col 91}{space 3} 1.348668
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .6408041{col 50}{space 2} .1391445{col 61}{space 1}   -2.05{col 70}{space 3}0.040{col 78}{space 4} .4186379{col 91}{space 3} .9808713
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .7738245{col 50}{space 2} .1608112{col 61}{space 1}   -1.23{col 70}{space 3}0.217{col 78}{space 4} .5148696{col 91}{space 3} 1.163022
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .5645058{col 50}{space 2} .1274512{col 61}{space 1}   -2.53{col 70}{space 3}0.011{col 78}{space 4} .3626017{col 91}{space 3} .8788345
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.240085{col 50}{space 2} .2334789{col 61}{space 1}    1.14{col 70}{space 3}0.253{col 78}{space 4} .8573168{col 91}{space 3} 1.793749
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.238847{col 50}{space 2} .3908825{col 61}{space 1}    0.68{col 70}{space 3}0.497{col 78}{space 4} .6673647{col 91}{space 3} 2.299703
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.287317{col 50}{space 2} .2907146{col 61}{space 1}    1.12{col 70}{space 3}0.263{col 78}{space 4} .8267987{col 91}{space 3} 2.004338
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 2.010093{col 50}{space 2} .5449394{col 61}{space 1}    2.58{col 70}{space 3}0.010{col 78}{space 4} 1.181364{col 91}{space 3} 3.420178
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .7500211{col 50}{space 2} .1116783{col 61}{space 1}   -1.93{col 70}{space 3}0.053{col 78}{space 4} .5601323{col 91}{space 3} 1.004284
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 2.376303{col 50}{space 2} .7231256{col 61}{space 1}    2.84{col 70}{space 3}0.004{col 78}{space 4} 1.308567{col 91}{space 3} 4.315265
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 2.171363{col 50}{space 2} .6616116{col 61}{space 1}    2.54{col 70}{space 3}0.011{col 78}{space 4} 1.194794{col 91}{space 3} 3.946133
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .8999696{col 50}{space 2} .3310424{col 61}{space 1}   -0.29{col 70}{space 3}0.774{col 78}{space 4} .4375508{col 91}{space 3} 1.851088
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.086634{col 50}{space 2} .2196671{col 61}{space 1}    0.41{col 70}{space 3}0.681{col 78}{space 4}  .731067{col 91}{space 3} 1.615137
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} .9521414{col 50}{space 2} .1698551{col 61}{space 1}   -0.27{col 70}{space 3}0.783{col 78}{space 4} .6711304{col 91}{space 3} 1.350815
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .0978468{col 50}{space 2} .0364748{col 61}{space 1}   -6.24{col 70}{space 3}0.000{col 78}{space 4} .0471133{col 91}{space 3} .2032123
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 2: Improve 
. svy: logit import_improve i.degree_dummy if infullmodel_TableD3 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   1{txt},{res}   3962{txt}){col 67}= {res}      0.06
{txt}{col 49}Prob > F{col 67}= {res}    0.8104

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}  Linearized
{col 1}import_improve{col 16}{c |} Odds Ratio{col 28}   Std. Err.{col 40}      t{col 48}   P>|t|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}degree_dummy {c |}
Degree-Holder  {c |}{col 16}{res}{space 2} 1.043895{col 28}{space 2} .1869199{col 39}{space 1}    0.24{col 48}{space 3}0.810{col 56}{space 4} .7348423{col 69}{space 3} 1.482927
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .0437079{col 28}{space 2} .0054302{col 39}{space 1}  -25.20{col 48}{space 3}0.000{col 56}{space 4}  .034259{col 69}{space 3} .0557629
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit import_improve i.degree_dummy $controls_demographics if infullmodel_TableD3 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      3.53
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                      import_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.022786{col 50}{space 2} .2006774{col 61}{space 1}    0.11{col 70}{space 3}0.909{col 78}{space 4} .6961809{col 91}{space 3} 1.502613
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .4813711{col 50}{space 2} .0989775{col 61}{space 1}   -3.56{col 70}{space 3}0.000{col 78}{space 4} .3216666{col 91}{space 3} .7203673
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .2059369{col 50}{space 2} .0579086{col 61}{space 1}   -5.62{col 70}{space 3}0.000{col 78}{space 4} .1186607{col 91}{space 3} .3574057
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}  .819794{col 50}{space 2}  .148332{col 61}{space 1}   -1.10{col 70}{space 3}0.272{col 78}{space 4} .5749669{col 91}{space 3} 1.168871
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .5329506{col 50}{space 2} .1449172{col 61}{space 1}   -2.31{col 70}{space 3}0.021{col 78}{space 4} .3127245{col 91}{space 3} .9082639
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .7019361{col 50}{space 2} .1437957{col 61}{space 1}   -1.73{col 70}{space 3}0.084{col 78}{space 4} .4697542{col 91}{space 3} 1.048877
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9356342{col 50}{space 2} .3569388{col 61}{space 1}   -0.17{col 70}{space 3}0.862{col 78}{space 4} .4428725{col 91}{space 3} 1.976667
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.143594{col 50}{space 2} .4221313{col 61}{space 1}    0.36{col 70}{space 3}0.716{col 78}{space 4} .5545935{col 91}{space 3} 2.358138
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.157162{col 50}{space 2} .5955343{col 61}{space 1}    0.28{col 70}{space 3}0.777{col 78}{space 4} .4218802{col 91}{space 3} 3.173946
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .3727353{col 50}{space 2}  .115085{col 61}{space 1}   -3.20{col 70}{space 3}0.001{col 78}{space 4} .2034721{col 91}{space 3}  .682804
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .3385937{col 50}{space 2}  .109652{col 61}{space 1}   -3.34{col 70}{space 3}0.001{col 78}{space 4} .1794474{col 91}{space 3}  .638882
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .4079394{col 50}{space 2} .1267116{col 61}{space 1}   -2.89{col 70}{space 3}0.004{col 78}{space 4}  .221881{col 91}{space 3} .7500174
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .2649332{col 50}{space 2} .0865925{col 61}{space 1}   -4.06{col 70}{space 3}0.000{col 78}{space 4} .1395851{col 91}{space 3} .5028447
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.498316{col 50}{space 2} .3613864{col 61}{space 1}    1.68{col 70}{space 3}0.094{col 78}{space 4} .9337597{col 91}{space 3} 2.404207
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}  .718643{col 50}{space 2} .2206643{col 61}{space 1}   -1.08{col 70}{space 3}0.282{col 78}{space 4} .3936099{col 91}{space 3}  1.31208
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .7920011{col 50}{space 2}  .249908{col 61}{space 1}   -0.74{col 70}{space 3}0.460{col 78}{space 4} .4266342{col 91}{space 3} 1.470266
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .9644461{col 50}{space 2} .3338255{col 61}{space 1}   -0.10{col 70}{space 3}0.917{col 78}{space 4} .4892828{col 91}{space 3} 1.901061
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .3241045{col 50}{space 2} .1221441{col 61}{space 1}   -2.99{col 70}{space 3}0.003{col 78}{space 4} .1548095{col 91}{space 3} .6785353
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit import_improve i.degree_dummy $controls_demographics $controls_objective if infullmodel_TableD3 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      2.81
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                      import_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.031034{col 50}{space 2} .2059519{col 61}{space 1}    0.15{col 70}{space 3}0.878{col 78}{space 4} .6969337{col 91}{space 3} 1.525299
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .4859691{col 50}{space 2}  .100689{col 61}{space 1}   -3.48{col 70}{space 3}0.001{col 78}{space 4} .3237371{col 91}{space 3} .7294992
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .2047739{col 50}{space 2} .0580106{col 61}{space 1}   -5.60{col 70}{space 3}0.000{col 78}{space 4} .1175069{col 91}{space 3} .3568502
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .8222797{col 50}{space 2} .1492892{col 61}{space 1}   -1.08{col 70}{space 3}0.281{col 78}{space 4} .5760129{col 91}{space 3} 1.173835
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2}  .532034{col 50}{space 2} .1453596{col 61}{space 1}   -2.31{col 70}{space 3}0.021{col 78}{space 4}  .311392{col 91}{space 3} .9090155
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .6794348{col 50}{space 2} .1425353{col 61}{space 1}   -1.84{col 70}{space 3}0.066{col 78}{space 4} .4503227{col 91}{space 3} 1.025113
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9105414{col 50}{space 2} .3478142{col 61}{space 1}   -0.25{col 70}{space 3}0.806{col 78}{space 4} .4305793{col 91}{space 3} 1.925512
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.146478{col 50}{space 2} .4223236{col 61}{space 1}    0.37{col 70}{space 3}0.711{col 78}{space 4} .5568215{col 91}{space 3} 2.360561
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.159751{col 50}{space 2} .5901265{col 61}{space 1}    0.29{col 70}{space 3}0.771{col 78}{space 4} .4276692{col 91}{space 3} 3.145007
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .3777598{col 50}{space 2} .1154978{col 61}{space 1}   -3.18{col 70}{space 3}0.001{col 78}{space 4} .2074371{col 91}{space 3} .6879312
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .3441002{col 50}{space 2} .1114877{col 61}{space 1}   -3.29{col 70}{space 3}0.001{col 78}{space 4} .1823111{col 91}{space 3} .6494664
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .4077277{col 50}{space 2} .1272669{col 61}{space 1}   -2.87{col 70}{space 3}0.004{col 78}{space 4} .2211045{col 91}{space 3} .7518703
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .2705826{col 50}{space 2} .0887859{col 61}{space 1}   -3.98{col 70}{space 3}0.000{col 78}{space 4} .1422037{col 91}{space 3} .5148598
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.453146{col 50}{space 2} .4086095{col 61}{space 1}    1.33{col 70}{space 3}0.184{col 78}{space 4} .8373113{col 91}{space 3}  2.52192
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.159795{col 50}{space 2}  .515801{col 61}{space 1}    0.33{col 70}{space 3}0.739{col 78}{space 4} .4849612{col 91}{space 3} 2.773676
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .7782851{col 50}{space 2} .3033448{col 61}{space 1}   -0.64{col 70}{space 3}0.520{col 78}{space 4} .3624704{col 91}{space 3} 1.671109
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .9747707{col 50}{space 2} .5281169{col 61}{space 1}   -0.05{col 70}{space 3}0.962{col 78}{space 4} .3369711{col 91}{space 3} 2.819761
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .9232136{col 50}{space 2} .2310483{col 61}{space 1}   -0.32{col 70}{space 3}0.750{col 78}{space 4} .5652115{col 91}{space 3} 1.507973
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} .9426035{col 50}{space 2} .4463135{col 61}{space 1}   -0.12{col 70}{space 3}0.901{col 78}{space 4} .3725377{col 91}{space 3} 2.384997
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.144866{col 50}{space 2} .5556917{col 61}{space 1}    0.28{col 70}{space 3}0.780{col 78}{space 4} .4420531{col 91}{space 3} 2.965068
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .5611062{col 50}{space 2} .3015494{col 61}{space 1}   -1.08{col 70}{space 3}0.282{col 78}{space 4} .1956379{col 91}{space 3} 1.609301
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.090376{col 50}{space 2} .3555053{col 61}{space 1}    0.27{col 70}{space 3}0.791{col 78}{space 4} .5753957{col 91}{space 3} 2.066266
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2}  1.36281{col 50}{space 2} .3875331{col 61}{space 1}    1.09{col 70}{space 3}0.276{col 78}{space 4} .7803887{col 91}{space 3} 2.379906
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2891603{col 50}{space 2} .1442222{col 61}{space 1}   -2.49{col 70}{space 3}0.013{col 78}{space 4} .1087581{col 91}{space 3} .7688037
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 3: Worsen v Improve
. svy: logit import_directionworse i.degree_dummy if infullmodel_TableD3 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       533
{txt}{col 1}Number of PSUs{col 20}= {res}      533{txt}{col 49}Population size{col 67}={res} 592.486062
{txt}{col 49}Design df{col 67}= {res}       532
{txt}{col 49}F({res}   1{txt},{res}    532{txt}){col 67}= {res}      0.91
{txt}{col 49}Prob > F{col 67}= {res}    0.3418

{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}  Linearized
{col 1}import_directionworse{col 23}{c |} Odds Ratio{col 35}   Std. Err.{col 47}      t{col 55}   P>|t|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}degree_dummy {c |}
{space 7}Degree-Holder  {c |}{col 23}{res}{space 2} .8196004{col 35}{space 2} .1713513{col 46}{space 1}   -0.95{col 55}{space 3}0.342{col 63}{space 4} .5435486{col 76}{space 3} 1.235851
{txt}{space 16}_cons {c |}{col 23}{res}{space 2} 2.615831{col 35}{space 2} .3742641{col 46}{space 1}    6.72{col 55}{space 3}0.000{col 63}{space 4}   1.9749{col 76}{space 3}  3.46477
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit import_directionworse i.degree_dummy $controls_demographics if infullmodel_TableD3 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       533
{txt}{col 1}Number of PSUs{col 20}= {res}      533{txt}{col 49}Population size{col 67}={res} 592.486062
{txt}{col 49}Design df{col 67}= {res}       532
{txt}{col 49}F({res}  17{txt},{res}    516{txt}){col 67}= {res}      2.28
{txt}{col 49}Prob > F{col 67}= {res}    0.0026

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}               import_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.101412{col 50}{space 2} .2589913{col 61}{space 1}    0.41{col 70}{space 3}0.681{col 78}{space 4} .6939658{col 91}{space 3} 1.748082
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 2.142296{col 50}{space 2} .5710227{col 61}{space 1}    2.86{col 70}{space 3}0.004{col 78}{space 4} 1.269044{col 91}{space 3} 3.616447
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 4.581218{col 50}{space 2} 1.514217{col 61}{space 1}    4.60{col 70}{space 3}0.000{col 78}{space 4} 2.393284{col 91}{space 3} 8.769355
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .7548422{col 50}{space 2} .1706943{col 61}{space 1}   -1.24{col 70}{space 3}0.214{col 78}{space 4} .4840977{col 91}{space 3} 1.177008
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.737503{col 50}{space 2}  .510699{col 61}{space 1}    1.88{col 70}{space 3}0.061{col 78}{space 4} .9753579{col 91}{space 3} 3.095189
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9391509{col 50}{space 2} .2415127{col 61}{space 1}   -0.24{col 70}{space 3}0.807{col 78}{space 4} .5666837{col 91}{space 3} 1.556432
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .8300581{col 50}{space 2} .3940198{col 61}{space 1}   -0.39{col 70}{space 3}0.695{col 78}{space 4} .3266862{col 91}{space 3} 2.109047
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.013492{col 50}{space 2} .4191468{col 61}{space 1}    0.03{col 70}{space 3}0.974{col 78}{space 4} .4497681{col 91}{space 3} 2.283767
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2}  .549621{col 50}{space 2} .3468396{col 61}{space 1}   -0.95{col 70}{space 3}0.343{col 78}{space 4} .1591057{col 91}{space 3} 1.898632
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.887767{col 50}{space 2} .6886964{col 61}{space 1}    1.74{col 70}{space 3}0.082{col 78}{space 4} .9219434{col 91}{space 3} 3.865381
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.645772{col 50}{space 2} .6414268{col 61}{space 1}    1.28{col 70}{space 3}0.202{col 78}{space 4}  .765357{col 91}{space 3} 3.538957
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.596431{col 50}{space 2} .5581033{col 61}{space 1}    1.34{col 70}{space 3}0.181{col 78}{space 4} .8033346{col 91}{space 3} 3.172517
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.797841{col 50}{space 2} .6174862{col 61}{space 1}    1.71{col 70}{space 3}0.088{col 78}{space 4}  .915653{col 91}{space 3} 3.529973
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .9201556{col 50}{space 2} .2876195{col 61}{space 1}   -0.27{col 70}{space 3}0.790{col 78}{space 4} .4979533{col 91}{space 3} 1.700333
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.676232{col 50}{space 2} .6010057{col 61}{space 1}    1.44{col 70}{space 3}0.150{col 78}{space 4} .8287885{col 91}{space 3} 3.390195
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.546733{col 50}{space 2} .5325191{col 61}{space 1}    1.27{col 70}{space 3}0.206{col 78}{space 4}  .786484{col 91}{space 3}  3.04187
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.556283{col 50}{space 2} .5677038{col 61}{space 1}    1.21{col 70}{space 3}0.226{col 78}{space 4} .7601124{col 91}{space 3} 3.186394
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .5793447{col 50}{space 2} .2391191{col 61}{space 1}   -1.32{col 70}{space 3}0.187{col 78}{space 4} .2575198{col 91}{space 3} 1.303357
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit import_directionworse i.degree_dummy $controls_demographics $controls_objective if infullmodel_TableD3 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       533
{txt}{col 1}Number of PSUs{col 20}= {res}      533{txt}{col 49}Population size{col 67}={res} 592.486062
{txt}{col 49}Design df{col 67}= {res}       532
{txt}{col 49}F({res}  23{txt},{res}    510{txt}){col 67}= {res}      1.76
{txt}{col 49}Prob > F{col 67}= {res}    0.0165

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}               import_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.080401{col 50}{space 2} .2550306{col 61}{space 1}    0.33{col 70}{space 3}0.743{col 78}{space 4} .6795148{col 91}{space 3} 1.717793
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 2.313153{col 50}{space 2} .6313656{col 61}{space 1}    3.07{col 70}{space 3}0.002{col 78}{space 4}  1.35314{col 91}{space 3} 3.954268
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 4.734086{col 50}{space 2} 1.610438{col 61}{space 1}    4.57{col 70}{space 3}0.000{col 78}{space 4} 2.426693{col 91}{space 3} 9.235438
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .7399686{col 50}{space 2} .1671828{col 61}{space 1}   -1.33{col 70}{space 3}0.183{col 78}{space 4} .4747455{col 91}{space 3} 1.153362
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.744612{col 50}{space 2} .5307557{col 61}{space 1}    1.83{col 70}{space 3}0.068{col 78}{space 4} .9597347{col 91}{space 3} 3.171368
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9074859{col 50}{space 2} .2415928{col 61}{space 1}   -0.36{col 70}{space 3}0.716{col 78}{space 4}  .537916{col 91}{space 3} 1.530965
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .7555217{col 50}{space 2} .3661597{col 61}{space 1}   -0.58{col 70}{space 3}0.563{col 78}{space 4} .2915922{col 91}{space 3} 1.957573
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2}   1.0286{col 50}{space 2} .4404442{col 61}{space 1}    0.07{col 70}{space 3}0.948{col 78}{space 4} .4435402{col 91}{space 3} 2.385395
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5997226{col 50}{space 2} .3572605{col 61}{space 1}   -0.86{col 70}{space 3}0.391{col 78}{space 4}  .186091{col 91}{space 3} 1.932749
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.779749{col 50}{space 2} .6540373{col 61}{space 1}    1.57{col 70}{space 3}0.117{col 78}{space 4} .8646471{col 91}{space 3} 3.663354
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.501792{col 50}{space 2} .5975836{col 61}{space 1}    1.02{col 70}{space 3}0.307{col 78}{space 4} .6872787{col 91}{space 3} 3.281609
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.504137{col 50}{space 2} .5283807{col 61}{space 1}    1.16{col 70}{space 3}0.246{col 78}{space 4} .7543821{col 91}{space 3} 2.999049
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.719397{col 50}{space 2} .5994877{col 61}{space 1}    1.55{col 70}{space 3}0.121{col 78}{space 4} .8667983{col 91}{space 3} 3.410626
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.015425{col 50}{space 2} .3558767{col 61}{space 1}    0.04{col 70}{space 3}0.965{col 78}{space 4} .5100897{col 91}{space 3} 2.021386
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.325183{col 50}{space 2} .6481626{col 61}{space 1}    0.58{col 70}{space 3}0.565{col 78}{space 4} .5069834{col 91}{space 3} 3.463841
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.595465{col 50}{space 2} .7151305{col 61}{space 1}    1.04{col 70}{space 3}0.298{col 78}{space 4} .6614326{col 91}{space 3} 3.848476
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.431199{col 50}{space 2} .8129854{col 61}{space 1}    0.63{col 70}{space 3}0.528{col 78}{space 4} .4688963{col 91}{space 3} 4.368409
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .8610504{col 50}{space 2}  .276007{col 61}{space 1}   -0.47{col 70}{space 3}0.641{col 78}{space 4} .4587292{col 91}{space 3} 1.616221
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.740808{col 50}{space 2} .9973024{col 61}{space 1}    0.97{col 70}{space 3}0.334{col 78}{space 4} .5649221{col 91}{space 3}   5.3643
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.508786{col 50}{space 2} .8598458{col 61}{space 1}    0.72{col 70}{space 3}0.471{col 78}{space 4} .4925246{col 91}{space 3}  4.62197
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} 1.317195{col 50}{space 2} .7961797{col 61}{space 1}    0.46{col 70}{space 3}0.649{col 78}{space 4} .4017605{col 91}{space 3} 4.318499
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .9289508{col 50}{space 2} .3635688{col 61}{space 1}   -0.19{col 70}{space 3}0.851{col 78}{space 4} .4306193{col 91}{space 3} 2.003973
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} .6042082{col 50}{space 2} .2071826{col 61}{space 1}   -1.47{col 70}{space 3}0.142{col 78}{space 4} .3080666{col 91}{space 3} 1.185028
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .5285986{col 50}{space 2} .2792063{col 61}{space 1}   -1.21{col 70}{space 3}0.228{col 78}{space 4} .1872817{col 91}{space 3} 1.491958
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. 
. ******* ISCO-08 Occupational Group ********
. 
. * Model 1: Worsen 
. svy: logit import_worsen i.isco08_5group if infullmodel_TableD3 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   4{txt},{res}   3959{txt}){col 67}= {res}      5.59
{txt}{col 49}Prob > F{col 67}= {res}    0.0002

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                     import_worsen{col 36}{c |} Odds Ratio{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}isco08_5group {c |}
Technician/Associate Professional  {c |}{col 36}{res}{space 2} 1.242687{col 48}{space 2} .2149983{col 59}{space 1}    1.26{col 68}{space 3}0.209{col 76}{space 4} .8852167{col 89}{space 3} 1.744511
{txt}{space 9}Clerical Support Workers  {c |}{col 36}{res}{space 2} 1.135693{col 48}{space 2} .2033203{col 59}{space 1}    0.71{col 68}{space 3}0.477{col 76}{space 4} .7995138{col 89}{space 3} 1.613229
{txt}{space 8}Service and Sales Workers  {c |}{col 36}{res}{space 2} 1.264303{col 48}{space 2} .2287151{col 59}{space 1}    1.30{col 68}{space 3}0.195{col 76}{space 4} .8867883{col 89}{space 3}  1.80253
{txt}{space 13}Manual Working Class  {c |}{col 36}{res}{space 2} 2.236317{col 48}{space 2} .3866599{col 59}{space 1}    4.65{col 68}{space 3}0.000{col 76}{space 4} 1.593366{col 89}{space 3} 3.138711
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .0898927{col 48}{space 2} .0089703{col 59}{space 1}  -24.14{col 68}{space 3}0.000{col 76}{space 4} .0739193{col 89}{space 3} .1093177
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit import_worsen i.isco08_5group $controls_demographics if infullmodel_TableD3 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      4.00
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                       import_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.255098{col 50}{space 2} .2202454{col 61}{space 1}    1.29{col 70}{space 3}0.195{col 78}{space 4} .8897394{col 91}{space 3} 1.770487
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.174341{col 50}{space 2} .2245627{col 61}{space 1}    0.84{col 70}{space 3}0.401{col 78}{space 4} .8071867{col 91}{space 3} 1.708498
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.295484{col 50}{space 2} .2566914{col 61}{space 1}    1.31{col 70}{space 3}0.191{col 78}{space 4} .8784575{col 91}{space 3} 1.910484
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.729091{col 50}{space 2} .3422669{col 61}{space 1}    2.77{col 70}{space 3}0.006{col 78}{space 4} 1.172936{col 91}{space 3} 2.548951
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.132824{col 50}{space 2} .2138252{col 61}{space 1}    0.66{col 70}{space 3}0.509{col 78}{space 4} .7824304{col 91}{space 3} 1.640134
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.085446{col 50}{space 2} .2166108{col 61}{space 1}    0.41{col 70}{space 3}0.681{col 78}{space 4} .7339914{col 91}{space 3} 1.605185
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6543863{col 50}{space 2} .0837702{col 61}{space 1}   -3.31{col 70}{space 3}0.001{col 78}{space 4} .5091382{col 91}{space 3} .8410709
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.113073{col 50}{space 2} .1462693{col 61}{space 1}    0.82{col 70}{space 3}0.415{col 78}{space 4} .8602666{col 91}{space 3} 1.440173
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .8912295{col 50}{space 2} .1472665{col 61}{space 1}   -0.70{col 70}{space 3}0.486{col 78}{space 4} .6446058{col 91}{space 3}  1.23221
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .5988927{col 50}{space 2} .0880808{col 61}{space 1}   -3.49{col 70}{space 3}0.000{col 78}{space 4} .4488717{col 91}{space 3} .7990534
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .8414512{col 50}{space 2}  .223756{col 61}{space 1}   -0.65{col 70}{space 3}0.516{col 78}{space 4} .4995868{col 91}{space 3} 1.417251
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.056759{col 50}{space 2} .2076127{col 61}{space 1}    0.28{col 70}{space 3}0.779{col 78}{space 4}  .718946{col 91}{space 3} 1.553301
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5350543{col 50}{space 2} .2184544{col 61}{space 1}   -1.53{col 70}{space 3}0.126{col 78}{space 4} .2403031{col 91}{space 3} 1.191342
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}  .879634{col 50}{space 2} .1907448{col 61}{space 1}   -0.59{col 70}{space 3}0.554{col 78}{space 4} .5749979{col 91}{space 3} 1.345668
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .6385776{col 50}{space 2} .1376237{col 61}{space 1}   -2.08{col 70}{space 3}0.037{col 78}{space 4} .4185141{col 91}{space 3} .9743552
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .7576182{col 50}{space 2} .1553592{col 61}{space 1}   -1.35{col 70}{space 3}0.176{col 78}{space 4} .5068127{col 91}{space 3}  1.13254
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .5546876{col 50}{space 2} .1239106{col 61}{space 1}   -2.64{col 70}{space 3}0.008{col 78}{space 4} .3579663{col 91}{space 3} .8595177
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .1640278{col 50}{space 2} .0453379{col 61}{space 1}   -6.54{col 70}{space 3}0.000{col 78}{space 4} .0954048{col 91}{space 3}   .28201
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit import_worsen i.isco08_5group $controls_demographics $controls_objective if infullmodel_TableD3 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      3.43
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                       import_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.240085{col 50}{space 2} .2334789{col 61}{space 1}    1.14{col 70}{space 3}0.253{col 78}{space 4} .8573168{col 91}{space 3} 1.793749
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.238847{col 50}{space 2} .3908825{col 61}{space 1}    0.68{col 70}{space 3}0.497{col 78}{space 4} .6673647{col 91}{space 3} 2.299703
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.287317{col 50}{space 2} .2907146{col 61}{space 1}    1.12{col 70}{space 3}0.263{col 78}{space 4} .8267987{col 91}{space 3} 2.004338
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 2.010093{col 50}{space 2} .5449394{col 61}{space 1}    2.58{col 70}{space 3}0.010{col 78}{space 4} 1.181364{col 91}{space 3} 3.420178
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2}  1.14489{col 50}{space 2} .2193418{col 61}{space 1}    0.71{col 70}{space 3}0.480{col 78}{space 4} .7863899{col 91}{space 3} 1.666824
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.091017{col 50}{space 2} .2200251{col 61}{space 1}    0.43{col 70}{space 3}0.666{col 78}{space 4} .7347125{col 91}{space 3} 1.620114
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}  .652949{col 50}{space 2} .0838384{col 61}{space 1}   -3.32{col 70}{space 3}0.001{col 78}{space 4} .5076354{col 91}{space 3} .8398595
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.106137{col 50}{space 2} .1468444{col 61}{space 1}    0.76{col 70}{space 3}0.447{col 78}{space 4}  .852656{col 91}{space 3} 1.434974
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .8677224{col 50}{space 2} .1436729{col 61}{space 1}   -0.86{col 70}{space 3}0.392{col 78}{space 4} .6271918{col 91}{space 3} 1.200498
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .5892368{col 50}{space 2} .0900676{col 61}{space 1}   -3.46{col 70}{space 3}0.001{col 78}{space 4} .4366566{col 91}{space 3} .7951329
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}  .849199{col 50}{space 2} .2281017{col 61}{space 1}   -0.61{col 70}{space 3}0.543{col 78}{space 4} .5015336{col 91}{space 3} 1.437868
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.072952{col 50}{space 2} .2137632{col 61}{space 1}    0.35{col 70}{space 3}0.724{col 78}{space 4} .7260127{col 91}{space 3} 1.585682
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5238108{col 50}{space 2} .2125478{col 61}{space 1}   -1.59{col 70}{space 3}0.111{col 78}{space 4}  .236415{col 91}{space 3} 1.160577
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .8804051{col 50}{space 2} .1915187{col 61}{space 1}   -0.59{col 70}{space 3}0.558{col 78}{space 4} .5747249{col 91}{space 3} 1.348668
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .6408041{col 50}{space 2} .1391445{col 61}{space 1}   -2.05{col 70}{space 3}0.040{col 78}{space 4} .4186379{col 91}{space 3} .9808713
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .7738245{col 50}{space 2} .1608112{col 61}{space 1}   -1.23{col 70}{space 3}0.217{col 78}{space 4} .5148696{col 91}{space 3} 1.163022
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .5645058{col 50}{space 2} .1274512{col 61}{space 1}   -2.53{col 70}{space 3}0.011{col 78}{space 4} .3626017{col 91}{space 3} .8788345
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .7500211{col 50}{space 2} .1116783{col 61}{space 1}   -1.93{col 70}{space 3}0.053{col 78}{space 4} .5601323{col 91}{space 3} 1.004284
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 2.376303{col 50}{space 2} .7231256{col 61}{space 1}    2.84{col 70}{space 3}0.004{col 78}{space 4} 1.308567{col 91}{space 3} 4.315265
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 2.171363{col 50}{space 2} .6616116{col 61}{space 1}    2.54{col 70}{space 3}0.011{col 78}{space 4} 1.194794{col 91}{space 3} 3.946133
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .8999696{col 50}{space 2} .3310424{col 61}{space 1}   -0.29{col 70}{space 3}0.774{col 78}{space 4} .4375508{col 91}{space 3} 1.851088
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.086634{col 50}{space 2} .2196671{col 61}{space 1}    0.41{col 70}{space 3}0.681{col 78}{space 4}  .731067{col 91}{space 3} 1.615137
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} .9521414{col 50}{space 2} .1698551{col 61}{space 1}   -0.27{col 70}{space 3}0.783{col 78}{space 4} .6711304{col 91}{space 3} 1.350815
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .0978468{col 50}{space 2} .0364748{col 61}{space 1}   -6.24{col 70}{space 3}0.000{col 78}{space 4} .0471133{col 91}{space 3} .2032123
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 2: Improve 
. svy: logit import_improve i.isco08_5group if infullmodel_TableD3 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   4{txt},{res}   3959{txt}){col 67}= {res}      1.93
{txt}{col 49}Prob > F{col 67}= {res}    0.1026

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                    import_improve{col 36}{c |} Odds Ratio{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}isco08_5group {c |}
Technician/Associate Professional  {c |}{col 36}{res}{space 2} 1.590921{col 48}{space 2} .3825184{col 59}{space 1}    1.93{col 68}{space 3}0.054{col 76}{space 4} .9929438{col 89}{space 3} 2.549017
{txt}{space 9}Clerical Support Workers  {c |}{col 36}{res}{space 2} .7290728{col 48}{space 2} .2051881{col 59}{space 1}   -1.12{col 68}{space 3}0.262{col 76}{space 4} .4198924{col 89}{space 3} 1.265913
{txt}{space 8}Service and Sales Workers  {c |}{col 36}{res}{space 2} .9128771{col 48}{space 2} .2386384{col 59}{space 1}   -0.35{col 68}{space 3}0.727{col 76}{space 4} .5468002{col 89}{space 3} 1.524038
{txt}{space 13}Manual Working Class  {c |}{col 36}{res}{space 2} 1.156178{col 48}{space 2} .3413046{col 59}{space 1}    0.49{col 68}{space 3}0.623{col 76}{space 4} .6481453{col 89}{space 3} 2.062418
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .0418826{col 48}{space 2} .0061479{col 59}{space 1}  -21.62{col 68}{space 3}0.000{col 76}{space 4} .0314086{col 89}{space 3} .0558494
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit import_improve i.isco08_5group $controls_demographics if infullmodel_TableD3 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      3.53
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                      import_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.498316{col 50}{space 2} .3613864{col 61}{space 1}    1.68{col 70}{space 3}0.094{col 78}{space 4} .9337597{col 91}{space 3} 2.404207
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}  .718643{col 50}{space 2} .2206643{col 61}{space 1}   -1.08{col 70}{space 3}0.282{col 78}{space 4} .3936099{col 91}{space 3}  1.31208
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .7920011{col 50}{space 2}  .249908{col 61}{space 1}   -0.74{col 70}{space 3}0.460{col 78}{space 4} .4266342{col 91}{space 3} 1.470266
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .9644461{col 50}{space 2} .3338255{col 61}{space 1}   -0.10{col 70}{space 3}0.917{col 78}{space 4} .4892828{col 91}{space 3} 1.901061
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .4813711{col 50}{space 2} .0989775{col 61}{space 1}   -3.56{col 70}{space 3}0.000{col 78}{space 4} .3216666{col 91}{space 3} .7203673
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .2059369{col 50}{space 2} .0579086{col 61}{space 1}   -5.62{col 70}{space 3}0.000{col 78}{space 4} .1186607{col 91}{space 3} .3574057
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}  .819794{col 50}{space 2}  .148332{col 61}{space 1}   -1.10{col 70}{space 3}0.272{col 78}{space 4} .5749669{col 91}{space 3} 1.168871
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.022786{col 50}{space 2} .2006774{col 61}{space 1}    0.11{col 70}{space 3}0.909{col 78}{space 4} .6961809{col 91}{space 3} 1.502613
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .5329506{col 50}{space 2} .1449172{col 61}{space 1}   -2.31{col 70}{space 3}0.021{col 78}{space 4} .3127245{col 91}{space 3} .9082639
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .7019361{col 50}{space 2} .1437957{col 61}{space 1}   -1.73{col 70}{space 3}0.084{col 78}{space 4} .4697542{col 91}{space 3} 1.048877
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9356342{col 50}{space 2} .3569388{col 61}{space 1}   -0.17{col 70}{space 3}0.862{col 78}{space 4} .4428725{col 91}{space 3} 1.976667
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.143594{col 50}{space 2} .4221313{col 61}{space 1}    0.36{col 70}{space 3}0.716{col 78}{space 4} .5545935{col 91}{space 3} 2.358138
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.157162{col 50}{space 2} .5955343{col 61}{space 1}    0.28{col 70}{space 3}0.777{col 78}{space 4} .4218802{col 91}{space 3} 3.173946
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .3727353{col 50}{space 2}  .115085{col 61}{space 1}   -3.20{col 70}{space 3}0.001{col 78}{space 4} .2034721{col 91}{space 3}  .682804
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .3385937{col 50}{space 2}  .109652{col 61}{space 1}   -3.34{col 70}{space 3}0.001{col 78}{space 4} .1794474{col 91}{space 3}  .638882
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .4079394{col 50}{space 2} .1267116{col 61}{space 1}   -2.89{col 70}{space 3}0.004{col 78}{space 4}  .221881{col 91}{space 3} .7500174
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .2649332{col 50}{space 2} .0865925{col 61}{space 1}   -4.06{col 70}{space 3}0.000{col 78}{space 4} .1395851{col 91}{space 3} .5028447
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .3241045{col 50}{space 2} .1221441{col 61}{space 1}   -2.99{col 70}{space 3}0.003{col 78}{space 4} .1548095{col 91}{space 3} .6785353
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit import_improve i.isco08_5group $controls_demographics $controls_objective if infullmodel_TableD3 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      2.81
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                      import_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.453146{col 50}{space 2} .4086095{col 61}{space 1}    1.33{col 70}{space 3}0.184{col 78}{space 4} .8373113{col 91}{space 3}  2.52192
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.159795{col 50}{space 2}  .515801{col 61}{space 1}    0.33{col 70}{space 3}0.739{col 78}{space 4} .4849612{col 91}{space 3} 2.773676
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .7782851{col 50}{space 2} .3033448{col 61}{space 1}   -0.64{col 70}{space 3}0.520{col 78}{space 4} .3624704{col 91}{space 3} 1.671109
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .9747707{col 50}{space 2} .5281169{col 61}{space 1}   -0.05{col 70}{space 3}0.962{col 78}{space 4} .3369711{col 91}{space 3} 2.819761
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .4859691{col 50}{space 2}  .100689{col 61}{space 1}   -3.48{col 70}{space 3}0.001{col 78}{space 4} .3237371{col 91}{space 3} .7294992
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .2047739{col 50}{space 2} .0580106{col 61}{space 1}   -5.60{col 70}{space 3}0.000{col 78}{space 4} .1175069{col 91}{space 3} .3568502
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .8222797{col 50}{space 2} .1492892{col 61}{space 1}   -1.08{col 70}{space 3}0.281{col 78}{space 4} .5760129{col 91}{space 3} 1.173835
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.031034{col 50}{space 2} .2059519{col 61}{space 1}    0.15{col 70}{space 3}0.878{col 78}{space 4} .6969337{col 91}{space 3} 1.525299
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2}  .532034{col 50}{space 2} .1453596{col 61}{space 1}   -2.31{col 70}{space 3}0.021{col 78}{space 4}  .311392{col 91}{space 3} .9090155
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .6794348{col 50}{space 2} .1425353{col 61}{space 1}   -1.84{col 70}{space 3}0.066{col 78}{space 4} .4503227{col 91}{space 3} 1.025113
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9105414{col 50}{space 2} .3478142{col 61}{space 1}   -0.25{col 70}{space 3}0.806{col 78}{space 4} .4305793{col 91}{space 3} 1.925512
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.146478{col 50}{space 2} .4223236{col 61}{space 1}    0.37{col 70}{space 3}0.711{col 78}{space 4} .5568215{col 91}{space 3} 2.360561
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.159751{col 50}{space 2} .5901265{col 61}{space 1}    0.29{col 70}{space 3}0.771{col 78}{space 4} .4276692{col 91}{space 3} 3.145007
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .3777598{col 50}{space 2} .1154978{col 61}{space 1}   -3.18{col 70}{space 3}0.001{col 78}{space 4} .2074371{col 91}{space 3} .6879312
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .3441002{col 50}{space 2} .1114877{col 61}{space 1}   -3.29{col 70}{space 3}0.001{col 78}{space 4} .1823111{col 91}{space 3} .6494664
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .4077277{col 50}{space 2} .1272669{col 61}{space 1}   -2.87{col 70}{space 3}0.004{col 78}{space 4} .2211045{col 91}{space 3} .7518703
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .2705826{col 50}{space 2} .0887859{col 61}{space 1}   -3.98{col 70}{space 3}0.000{col 78}{space 4} .1422037{col 91}{space 3} .5148598
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .9232136{col 50}{space 2} .2310483{col 61}{space 1}   -0.32{col 70}{space 3}0.750{col 78}{space 4} .5652115{col 91}{space 3} 1.507973
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} .9426035{col 50}{space 2} .4463135{col 61}{space 1}   -0.12{col 70}{space 3}0.901{col 78}{space 4} .3725377{col 91}{space 3} 2.384997
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.144866{col 50}{space 2} .5556917{col 61}{space 1}    0.28{col 70}{space 3}0.780{col 78}{space 4} .4420531{col 91}{space 3} 2.965068
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .5611062{col 50}{space 2} .3015494{col 61}{space 1}   -1.08{col 70}{space 3}0.282{col 78}{space 4} .1956379{col 91}{space 3} 1.609301
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.090376{col 50}{space 2} .3555053{col 61}{space 1}    0.27{col 70}{space 3}0.791{col 78}{space 4} .5753957{col 91}{space 3} 2.066266
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2}  1.36281{col 50}{space 2} .3875331{col 61}{space 1}    1.09{col 70}{space 3}0.276{col 78}{space 4} .7803887{col 91}{space 3} 2.379906
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2891603{col 50}{space 2} .1442222{col 61}{space 1}   -2.49{col 70}{space 3}0.013{col 78}{space 4} .1087581{col 91}{space 3} .7688037
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 3: Worsen v Improve
. svy: logit import_directionworse i.isco08_5group if infullmodel_TableD3 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       533
{txt}{col 1}Number of PSUs{col 20}= {res}      533{txt}{col 49}Population size{col 67}={res} 592.486062
{txt}{col 49}Design df{col 67}= {res}       532
{txt}{col 49}F({res}   4{txt},{res}    529{txt}){col 67}= {res}      1.79
{txt}{col 49}Prob > F{col 67}= {res}    0.1303

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}             import_directionworse{col 36}{c |} Odds Ratio{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}isco08_5group {c |}
Technician/Associate Professional  {c |}{col 36}{res}{space 2} .7839739{col 48}{space 2} .2252766{col 59}{space 1}   -0.85{col 68}{space 3}0.397{col 76}{space 4} .4458095{col 89}{space 3}  1.37865
{txt}{space 9}Clerical Support Workers  {c |}{col 36}{res}{space 2} 1.523705{col 48}{space 2} .4960959{col 59}{space 1}    1.29{col 68}{space 3}0.196{col 76}{space 4} .8037668{col 89}{space 3} 2.888494
{txt}{space 8}Service and Sales Workers  {c |}{col 36}{res}{space 2} 1.350671{col 48}{space 2} .4169609{col 59}{space 1}    0.97{col 68}{space 3}0.331{col 76}{space 4} .7365107{col 89}{space 3} 2.476967
{txt}{space 13}Manual Working Class  {c |}{col 36}{res}{space 2} 1.766271{col 48}{space 2} .5854098{col 59}{space 1}    1.72{col 68}{space 3}0.087{col 76}{space 4} .9210714{col 89}{space 3} 3.387048
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} 2.051757{col 48}{space 2} .3551653{col 59}{space 1}    4.15{col 68}{space 3}0.000{col 76}{space 4} 1.460307{col 89}{space 3} 2.882753
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit import_directionworse i.isco08_5group $controls_demographics if infullmodel_TableD3 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       533
{txt}{col 1}Number of PSUs{col 20}= {res}      533{txt}{col 49}Population size{col 67}={res} 592.486062
{txt}{col 49}Design df{col 67}= {res}       532
{txt}{col 49}F({res}  17{txt},{res}    516{txt}){col 67}= {res}      2.28
{txt}{col 49}Prob > F{col 67}= {res}    0.0026

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}               import_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .9201556{col 50}{space 2} .2876195{col 61}{space 1}   -0.27{col 70}{space 3}0.790{col 78}{space 4} .4979533{col 91}{space 3} 1.700333
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.676232{col 50}{space 2} .6010057{col 61}{space 1}    1.44{col 70}{space 3}0.150{col 78}{space 4} .8287885{col 91}{space 3} 3.390195
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.546733{col 50}{space 2} .5325191{col 61}{space 1}    1.27{col 70}{space 3}0.206{col 78}{space 4}  .786484{col 91}{space 3}  3.04187
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.556283{col 50}{space 2} .5677038{col 61}{space 1}    1.21{col 70}{space 3}0.226{col 78}{space 4} .7601124{col 91}{space 3} 3.186394
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 2.142296{col 50}{space 2} .5710227{col 61}{space 1}    2.86{col 70}{space 3}0.004{col 78}{space 4} 1.269044{col 91}{space 3} 3.616447
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 4.581218{col 50}{space 2} 1.514217{col 61}{space 1}    4.60{col 70}{space 3}0.000{col 78}{space 4} 2.393284{col 91}{space 3} 8.769355
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .7548422{col 50}{space 2} .1706943{col 61}{space 1}   -1.24{col 70}{space 3}0.214{col 78}{space 4} .4840977{col 91}{space 3} 1.177008
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.101412{col 50}{space 2} .2589913{col 61}{space 1}    0.41{col 70}{space 3}0.681{col 78}{space 4} .6939658{col 91}{space 3} 1.748082
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.737503{col 50}{space 2}  .510699{col 61}{space 1}    1.88{col 70}{space 3}0.061{col 78}{space 4} .9753579{col 91}{space 3} 3.095189
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9391509{col 50}{space 2} .2415127{col 61}{space 1}   -0.24{col 70}{space 3}0.807{col 78}{space 4} .5666837{col 91}{space 3} 1.556432
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .8300581{col 50}{space 2} .3940198{col 61}{space 1}   -0.39{col 70}{space 3}0.695{col 78}{space 4} .3266862{col 91}{space 3} 2.109047
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.013492{col 50}{space 2} .4191468{col 61}{space 1}    0.03{col 70}{space 3}0.974{col 78}{space 4} .4497681{col 91}{space 3} 2.283767
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2}  .549621{col 50}{space 2} .3468396{col 61}{space 1}   -0.95{col 70}{space 3}0.343{col 78}{space 4} .1591057{col 91}{space 3} 1.898632
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.887767{col 50}{space 2} .6886964{col 61}{space 1}    1.74{col 70}{space 3}0.082{col 78}{space 4} .9219434{col 91}{space 3} 3.865381
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.645772{col 50}{space 2} .6414268{col 61}{space 1}    1.28{col 70}{space 3}0.202{col 78}{space 4}  .765357{col 91}{space 3} 3.538957
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.596431{col 50}{space 2} .5581033{col 61}{space 1}    1.34{col 70}{space 3}0.181{col 78}{space 4} .8033346{col 91}{space 3} 3.172517
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.797841{col 50}{space 2} .6174862{col 61}{space 1}    1.71{col 70}{space 3}0.088{col 78}{space 4}  .915653{col 91}{space 3} 3.529973
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .5793447{col 50}{space 2} .2391191{col 61}{space 1}   -1.32{col 70}{space 3}0.187{col 78}{space 4} .2575198{col 91}{space 3} 1.303357
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit import_directionworse i.isco08_5group $controls_demographics $controls_objective if infullmodel_TableD3 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       533
{txt}{col 1}Number of PSUs{col 20}= {res}      533{txt}{col 49}Population size{col 67}={res} 592.486062
{txt}{col 49}Design df{col 67}= {res}       532
{txt}{col 49}F({res}  23{txt},{res}    510{txt}){col 67}= {res}      1.76
{txt}{col 49}Prob > F{col 67}= {res}    0.0165

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}               import_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.015425{col 50}{space 2} .3558767{col 61}{space 1}    0.04{col 70}{space 3}0.965{col 78}{space 4} .5100897{col 91}{space 3} 2.021386
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.325183{col 50}{space 2} .6481626{col 61}{space 1}    0.58{col 70}{space 3}0.565{col 78}{space 4} .5069834{col 91}{space 3} 3.463841
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.595465{col 50}{space 2} .7151305{col 61}{space 1}    1.04{col 70}{space 3}0.298{col 78}{space 4} .6614326{col 91}{space 3} 3.848476
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.431199{col 50}{space 2} .8129854{col 61}{space 1}    0.63{col 70}{space 3}0.528{col 78}{space 4} .4688963{col 91}{space 3} 4.368409
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 2.313153{col 50}{space 2} .6313656{col 61}{space 1}    3.07{col 70}{space 3}0.002{col 78}{space 4}  1.35314{col 91}{space 3} 3.954268
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 4.734086{col 50}{space 2} 1.610438{col 61}{space 1}    4.57{col 70}{space 3}0.000{col 78}{space 4} 2.426693{col 91}{space 3} 9.235438
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .7399686{col 50}{space 2} .1671828{col 61}{space 1}   -1.33{col 70}{space 3}0.183{col 78}{space 4} .4747455{col 91}{space 3} 1.153362
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.080401{col 50}{space 2} .2550306{col 61}{space 1}    0.33{col 70}{space 3}0.743{col 78}{space 4} .6795148{col 91}{space 3} 1.717793
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.744612{col 50}{space 2} .5307557{col 61}{space 1}    1.83{col 70}{space 3}0.068{col 78}{space 4} .9597347{col 91}{space 3} 3.171368
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9074859{col 50}{space 2} .2415928{col 61}{space 1}   -0.36{col 70}{space 3}0.716{col 78}{space 4}  .537916{col 91}{space 3} 1.530965
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .7555217{col 50}{space 2} .3661597{col 61}{space 1}   -0.58{col 70}{space 3}0.563{col 78}{space 4} .2915922{col 91}{space 3} 1.957573
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2}   1.0286{col 50}{space 2} .4404442{col 61}{space 1}    0.07{col 70}{space 3}0.948{col 78}{space 4} .4435402{col 91}{space 3} 2.385395
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5997226{col 50}{space 2} .3572605{col 61}{space 1}   -0.86{col 70}{space 3}0.391{col 78}{space 4}  .186091{col 91}{space 3} 1.932749
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.779749{col 50}{space 2} .6540373{col 61}{space 1}    1.57{col 70}{space 3}0.117{col 78}{space 4} .8646471{col 91}{space 3} 3.663354
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.501792{col 50}{space 2} .5975836{col 61}{space 1}    1.02{col 70}{space 3}0.307{col 78}{space 4} .6872787{col 91}{space 3} 3.281609
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.504137{col 50}{space 2} .5283807{col 61}{space 1}    1.16{col 70}{space 3}0.246{col 78}{space 4} .7543821{col 91}{space 3} 2.999049
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.719397{col 50}{space 2} .5994877{col 61}{space 1}    1.55{col 70}{space 3}0.121{col 78}{space 4} .8667983{col 91}{space 3} 3.410626
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .8610504{col 50}{space 2}  .276007{col 61}{space 1}   -0.47{col 70}{space 3}0.641{col 78}{space 4} .4587292{col 91}{space 3} 1.616221
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.740808{col 50}{space 2} .9973024{col 61}{space 1}    0.97{col 70}{space 3}0.334{col 78}{space 4} .5649221{col 91}{space 3}   5.3643
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.508786{col 50}{space 2} .8598458{col 61}{space 1}    0.72{col 70}{space 3}0.471{col 78}{space 4} .4925246{col 91}{space 3}  4.62197
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} 1.317195{col 50}{space 2} .7961797{col 61}{space 1}    0.46{col 70}{space 3}0.649{col 78}{space 4} .4017605{col 91}{space 3} 4.318499
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .9289508{col 50}{space 2} .3635688{col 61}{space 1}   -0.19{col 70}{space 3}0.851{col 78}{space 4} .4306193{col 91}{space 3} 2.003973
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} .6042082{col 50}{space 2} .2071826{col 61}{space 1}   -1.47{col 70}{space 3}0.142{col 78}{space 4} .3080666{col 91}{space 3} 1.185028
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .5285986{col 50}{space 2} .2792063{col 61}{space 1}   -1.21{col 70}{space 3}0.228{col 78}{space 4} .1872817{col 91}{space 3} 1.491958
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. 
. ******* Employment Sector Group ********
. 
. * Model 1: Worsen 
. svy: logit import_worsen i.employsector5 if infullmodel_TableD3 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   4{txt},{res}   3959{txt}){col 67}= {res}      5.71
{txt}{col 49}Prob > F{col 67}= {res}    0.0001

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                       import_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .5433345{col 50}{space 2} .0783472{col 61}{space 1}   -4.23{col 70}{space 3}0.000{col 78}{space 4} .4095331{col 91}{space 3}  .720851
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .6851366{col 50}{space 2} .1837145{col 61}{space 1}   -1.41{col 70}{space 3}0.159{col 78}{space 4} .4050082{col 91}{space 3} 1.159019
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.119643{col 50}{space 2} .2047046{col 61}{space 1}    0.62{col 70}{space 3}0.537{col 78}{space 4} .7823601{col 91}{space 3} 1.602332
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5389585{col 50}{space 2} .2181364{col 61}{space 1}   -1.53{col 70}{space 3}0.127{col 78}{space 4} .2437459{col 91}{space 3} 1.191718
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .1381908{col 50}{space 2} .0110267{col 61}{space 1}  -24.80{col 70}{space 3}0.000{col 78}{space 4} .1181785{col 91}{space 3}  .161592
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit import_worsen i.employsector5 $controls_demographics if infullmodel_TableD3 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      4.00
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                       import_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .5988927{col 50}{space 2} .0880808{col 61}{space 1}   -3.49{col 70}{space 3}0.000{col 78}{space 4} .4488717{col 91}{space 3} .7990534
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .8414512{col 50}{space 2}  .223756{col 61}{space 1}   -0.65{col 70}{space 3}0.516{col 78}{space 4} .4995868{col 91}{space 3} 1.417251
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.056759{col 50}{space 2} .2076127{col 61}{space 1}    0.28{col 70}{space 3}0.779{col 78}{space 4}  .718946{col 91}{space 3} 1.553301
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5350543{col 50}{space 2} .2184544{col 61}{space 1}   -1.53{col 70}{space 3}0.126{col 78}{space 4} .2403031{col 91}{space 3} 1.191342
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.132824{col 50}{space 2} .2138252{col 61}{space 1}    0.66{col 70}{space 3}0.509{col 78}{space 4} .7824304{col 91}{space 3} 1.640134
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.085446{col 50}{space 2} .2166108{col 61}{space 1}    0.41{col 70}{space 3}0.681{col 78}{space 4} .7339914{col 91}{space 3} 1.605185
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6543863{col 50}{space 2} .0837702{col 61}{space 1}   -3.31{col 70}{space 3}0.001{col 78}{space 4} .5091382{col 91}{space 3} .8410709
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.113073{col 50}{space 2} .1462693{col 61}{space 1}    0.82{col 70}{space 3}0.415{col 78}{space 4} .8602666{col 91}{space 3} 1.440173
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .8912295{col 50}{space 2} .1472665{col 61}{space 1}   -0.70{col 70}{space 3}0.486{col 78}{space 4} .6446058{col 91}{space 3}  1.23221
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}  .879634{col 50}{space 2} .1907448{col 61}{space 1}   -0.59{col 70}{space 3}0.554{col 78}{space 4} .5749979{col 91}{space 3} 1.345668
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .6385776{col 50}{space 2} .1376237{col 61}{space 1}   -2.08{col 70}{space 3}0.037{col 78}{space 4} .4185141{col 91}{space 3} .9743552
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .7576182{col 50}{space 2} .1553592{col 61}{space 1}   -1.35{col 70}{space 3}0.176{col 78}{space 4} .5068127{col 91}{space 3}  1.13254
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .5546876{col 50}{space 2} .1239106{col 61}{space 1}   -2.64{col 70}{space 3}0.008{col 78}{space 4} .3579663{col 91}{space 3} .8595177
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.255098{col 50}{space 2} .2202454{col 61}{space 1}    1.29{col 70}{space 3}0.195{col 78}{space 4} .8897394{col 91}{space 3} 1.770487
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.174341{col 50}{space 2} .2245627{col 61}{space 1}    0.84{col 70}{space 3}0.401{col 78}{space 4} .8071867{col 91}{space 3} 1.708498
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.295484{col 50}{space 2} .2566914{col 61}{space 1}    1.31{col 70}{space 3}0.191{col 78}{space 4} .8784575{col 91}{space 3} 1.910484
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.729091{col 50}{space 2} .3422669{col 61}{space 1}    2.77{col 70}{space 3}0.006{col 78}{space 4} 1.172936{col 91}{space 3} 2.548951
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .1640278{col 50}{space 2} .0453379{col 61}{space 1}   -6.54{col 70}{space 3}0.000{col 78}{space 4} .0954048{col 91}{space 3}   .28201
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit import_worsen i.employsector5 $controls_demographics $controls_objective if infullmodel_TableD3 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      3.43
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                       import_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .5892368{col 50}{space 2} .0900676{col 61}{space 1}   -3.46{col 70}{space 3}0.001{col 78}{space 4} .4366566{col 91}{space 3} .7951329
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}  .849199{col 50}{space 2} .2281017{col 61}{space 1}   -0.61{col 70}{space 3}0.543{col 78}{space 4} .5015336{col 91}{space 3} 1.437868
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.072952{col 50}{space 2} .2137632{col 61}{space 1}    0.35{col 70}{space 3}0.724{col 78}{space 4} .7260127{col 91}{space 3} 1.585682
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5238108{col 50}{space 2} .2125478{col 61}{space 1}   -1.59{col 70}{space 3}0.111{col 78}{space 4}  .236415{col 91}{space 3} 1.160577
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2}  1.14489{col 50}{space 2} .2193418{col 61}{space 1}    0.71{col 70}{space 3}0.480{col 78}{space 4} .7863899{col 91}{space 3} 1.666824
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.091017{col 50}{space 2} .2200251{col 61}{space 1}    0.43{col 70}{space 3}0.666{col 78}{space 4} .7347125{col 91}{space 3} 1.620114
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}  .652949{col 50}{space 2} .0838384{col 61}{space 1}   -3.32{col 70}{space 3}0.001{col 78}{space 4} .5076354{col 91}{space 3} .8398595
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.106137{col 50}{space 2} .1468444{col 61}{space 1}    0.76{col 70}{space 3}0.447{col 78}{space 4}  .852656{col 91}{space 3} 1.434974
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .8677224{col 50}{space 2} .1436729{col 61}{space 1}   -0.86{col 70}{space 3}0.392{col 78}{space 4} .6271918{col 91}{space 3} 1.200498
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .8804051{col 50}{space 2} .1915187{col 61}{space 1}   -0.59{col 70}{space 3}0.558{col 78}{space 4} .5747249{col 91}{space 3} 1.348668
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .6408041{col 50}{space 2} .1391445{col 61}{space 1}   -2.05{col 70}{space 3}0.040{col 78}{space 4} .4186379{col 91}{space 3} .9808713
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .7738245{col 50}{space 2} .1608112{col 61}{space 1}   -1.23{col 70}{space 3}0.217{col 78}{space 4} .5148696{col 91}{space 3} 1.163022
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .5645058{col 50}{space 2} .1274512{col 61}{space 1}   -2.53{col 70}{space 3}0.011{col 78}{space 4} .3626017{col 91}{space 3} .8788345
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.240085{col 50}{space 2} .2334789{col 61}{space 1}    1.14{col 70}{space 3}0.253{col 78}{space 4} .8573168{col 91}{space 3} 1.793749
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.238847{col 50}{space 2} .3908825{col 61}{space 1}    0.68{col 70}{space 3}0.497{col 78}{space 4} .6673647{col 91}{space 3} 2.299703
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.287317{col 50}{space 2} .2907146{col 61}{space 1}    1.12{col 70}{space 3}0.263{col 78}{space 4} .8267987{col 91}{space 3} 2.004338
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 2.010093{col 50}{space 2} .5449394{col 61}{space 1}    2.58{col 70}{space 3}0.010{col 78}{space 4} 1.181364{col 91}{space 3} 3.420178
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .7500211{col 50}{space 2} .1116783{col 61}{space 1}   -1.93{col 70}{space 3}0.053{col 78}{space 4} .5601323{col 91}{space 3} 1.004284
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 2.376303{col 50}{space 2} .7231256{col 61}{space 1}    2.84{col 70}{space 3}0.004{col 78}{space 4} 1.308567{col 91}{space 3} 4.315265
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 2.171363{col 50}{space 2} .6616116{col 61}{space 1}    2.54{col 70}{space 3}0.011{col 78}{space 4} 1.194794{col 91}{space 3} 3.946133
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .8999696{col 50}{space 2} .3310424{col 61}{space 1}   -0.29{col 70}{space 3}0.774{col 78}{space 4} .4375508{col 91}{space 3} 1.851088
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.086634{col 50}{space 2} .2196671{col 61}{space 1}    0.41{col 70}{space 3}0.681{col 78}{space 4}  .731067{col 91}{space 3} 1.615137
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} .9521414{col 50}{space 2} .1698551{col 61}{space 1}   -0.27{col 70}{space 3}0.783{col 78}{space 4} .6711304{col 91}{space 3} 1.350815
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .0978468{col 50}{space 2} .0364748{col 61}{space 1}   -6.24{col 70}{space 3}0.000{col 78}{space 4} .0471133{col 91}{space 3} .2032123
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 2: Improve 
. svy: logit import_improve i.employsector5 if infullmodel_TableD3 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   4{txt},{res}   3959{txt}){col 67}= {res}      1.00
{txt}{col 49}Prob > F{col 67}= {res}    0.4050

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                      import_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .6798368{col 50}{space 2} .1381472{col 61}{space 1}   -1.90{col 70}{space 3}0.058{col 78}{space 4} .4564384{col 91}{space 3} 1.012575
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .8104112{col 50}{space 2} .3071465{col 61}{space 1}   -0.55{col 70}{space 3}0.579{col 78}{space 4}  .385479{col 91}{space 3} 1.703767
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .9019306{col 50}{space 2} .3054346{col 61}{space 1}   -0.30{col 70}{space 3}0.761{col 78}{space 4}  .464333{col 91}{space 3}  1.75193
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.153586{col 50}{space 2} .5626675{col 61}{space 1}    0.29{col 70}{space 3}0.770{col 78}{space 4} .4433484{col 91}{space 3} 3.001613
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .0508774{col 50}{space 2} .0062646{col 61}{space 1}  -24.19{col 70}{space 3}0.000{col 78}{space 4} .0399653{col 91}{space 3} .0647689
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit import_improve i.employsector5 $controls_demographics if infullmodel_TableD3 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      3.53
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                      import_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .7019361{col 50}{space 2} .1437957{col 61}{space 1}   -1.73{col 70}{space 3}0.084{col 78}{space 4} .4697542{col 91}{space 3} 1.048877
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9356342{col 50}{space 2} .3569388{col 61}{space 1}   -0.17{col 70}{space 3}0.862{col 78}{space 4} .4428725{col 91}{space 3} 1.976667
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.143594{col 50}{space 2} .4221313{col 61}{space 1}    0.36{col 70}{space 3}0.716{col 78}{space 4} .5545935{col 91}{space 3} 2.358138
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.157162{col 50}{space 2} .5955343{col 61}{space 1}    0.28{col 70}{space 3}0.777{col 78}{space 4} .4218802{col 91}{space 3} 3.173946
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .4813711{col 50}{space 2} .0989775{col 61}{space 1}   -3.56{col 70}{space 3}0.000{col 78}{space 4} .3216666{col 91}{space 3} .7203673
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .2059369{col 50}{space 2} .0579086{col 61}{space 1}   -5.62{col 70}{space 3}0.000{col 78}{space 4} .1186607{col 91}{space 3} .3574057
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}  .819794{col 50}{space 2}  .148332{col 61}{space 1}   -1.10{col 70}{space 3}0.272{col 78}{space 4} .5749669{col 91}{space 3} 1.168871
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.022786{col 50}{space 2} .2006774{col 61}{space 1}    0.11{col 70}{space 3}0.909{col 78}{space 4} .6961809{col 91}{space 3} 1.502613
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .5329506{col 50}{space 2} .1449172{col 61}{space 1}   -2.31{col 70}{space 3}0.021{col 78}{space 4} .3127245{col 91}{space 3} .9082639
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .3727353{col 50}{space 2}  .115085{col 61}{space 1}   -3.20{col 70}{space 3}0.001{col 78}{space 4} .2034721{col 91}{space 3}  .682804
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .3385937{col 50}{space 2}  .109652{col 61}{space 1}   -3.34{col 70}{space 3}0.001{col 78}{space 4} .1794474{col 91}{space 3}  .638882
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .4079394{col 50}{space 2} .1267116{col 61}{space 1}   -2.89{col 70}{space 3}0.004{col 78}{space 4}  .221881{col 91}{space 3} .7500174
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .2649332{col 50}{space 2} .0865925{col 61}{space 1}   -4.06{col 70}{space 3}0.000{col 78}{space 4} .1395851{col 91}{space 3} .5028447
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.498316{col 50}{space 2} .3613864{col 61}{space 1}    1.68{col 70}{space 3}0.094{col 78}{space 4} .9337597{col 91}{space 3} 2.404207
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}  .718643{col 50}{space 2} .2206643{col 61}{space 1}   -1.08{col 70}{space 3}0.282{col 78}{space 4} .3936099{col 91}{space 3}  1.31208
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .7920011{col 50}{space 2}  .249908{col 61}{space 1}   -0.74{col 70}{space 3}0.460{col 78}{space 4} .4266342{col 91}{space 3} 1.470266
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .9644461{col 50}{space 2} .3338255{col 61}{space 1}   -0.10{col 70}{space 3}0.917{col 78}{space 4} .4892828{col 91}{space 3} 1.901061
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .3241045{col 50}{space 2} .1221441{col 61}{space 1}   -2.99{col 70}{space 3}0.003{col 78}{space 4} .1548095{col 91}{space 3} .6785353
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit import_improve i.employsector5 $controls_demographics $controls_objective if infullmodel_TableD3 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      2.81
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                      import_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .6794348{col 50}{space 2} .1425353{col 61}{space 1}   -1.84{col 70}{space 3}0.066{col 78}{space 4} .4503227{col 91}{space 3} 1.025113
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9105414{col 50}{space 2} .3478142{col 61}{space 1}   -0.25{col 70}{space 3}0.806{col 78}{space 4} .4305793{col 91}{space 3} 1.925512
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.146478{col 50}{space 2} .4223236{col 61}{space 1}    0.37{col 70}{space 3}0.711{col 78}{space 4} .5568215{col 91}{space 3} 2.360561
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.159751{col 50}{space 2} .5901265{col 61}{space 1}    0.29{col 70}{space 3}0.771{col 78}{space 4} .4276692{col 91}{space 3} 3.145007
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .4859691{col 50}{space 2}  .100689{col 61}{space 1}   -3.48{col 70}{space 3}0.001{col 78}{space 4} .3237371{col 91}{space 3} .7294992
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .2047739{col 50}{space 2} .0580106{col 61}{space 1}   -5.60{col 70}{space 3}0.000{col 78}{space 4} .1175069{col 91}{space 3} .3568502
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .8222797{col 50}{space 2} .1492892{col 61}{space 1}   -1.08{col 70}{space 3}0.281{col 78}{space 4} .5760129{col 91}{space 3} 1.173835
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.031034{col 50}{space 2} .2059519{col 61}{space 1}    0.15{col 70}{space 3}0.878{col 78}{space 4} .6969337{col 91}{space 3} 1.525299
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2}  .532034{col 50}{space 2} .1453596{col 61}{space 1}   -2.31{col 70}{space 3}0.021{col 78}{space 4}  .311392{col 91}{space 3} .9090155
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .3777598{col 50}{space 2} .1154978{col 61}{space 1}   -3.18{col 70}{space 3}0.001{col 78}{space 4} .2074371{col 91}{space 3} .6879312
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .3441002{col 50}{space 2} .1114877{col 61}{space 1}   -3.29{col 70}{space 3}0.001{col 78}{space 4} .1823111{col 91}{space 3} .6494664
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .4077277{col 50}{space 2} .1272669{col 61}{space 1}   -2.87{col 70}{space 3}0.004{col 78}{space 4} .2211045{col 91}{space 3} .7518703
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .2705826{col 50}{space 2} .0887859{col 61}{space 1}   -3.98{col 70}{space 3}0.000{col 78}{space 4} .1422037{col 91}{space 3} .5148598
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.453146{col 50}{space 2} .4086095{col 61}{space 1}    1.33{col 70}{space 3}0.184{col 78}{space 4} .8373113{col 91}{space 3}  2.52192
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.159795{col 50}{space 2}  .515801{col 61}{space 1}    0.33{col 70}{space 3}0.739{col 78}{space 4} .4849612{col 91}{space 3} 2.773676
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .7782851{col 50}{space 2} .3033448{col 61}{space 1}   -0.64{col 70}{space 3}0.520{col 78}{space 4} .3624704{col 91}{space 3} 1.671109
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .9747707{col 50}{space 2} .5281169{col 61}{space 1}   -0.05{col 70}{space 3}0.962{col 78}{space 4} .3369711{col 91}{space 3} 2.819761
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .9232136{col 50}{space 2} .2310483{col 61}{space 1}   -0.32{col 70}{space 3}0.750{col 78}{space 4} .5652115{col 91}{space 3} 1.507973
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} .9426035{col 50}{space 2} .4463135{col 61}{space 1}   -0.12{col 70}{space 3}0.901{col 78}{space 4} .3725377{col 91}{space 3} 2.384997
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.144866{col 50}{space 2} .5556917{col 61}{space 1}    0.28{col 70}{space 3}0.780{col 78}{space 4} .4420531{col 91}{space 3} 2.965068
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .5611062{col 50}{space 2} .3015494{col 61}{space 1}   -1.08{col 70}{space 3}0.282{col 78}{space 4} .1956379{col 91}{space 3} 1.609301
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.090376{col 50}{space 2} .3555053{col 61}{space 1}    0.27{col 70}{space 3}0.791{col 78}{space 4} .5753957{col 91}{space 3} 2.066266
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2}  1.36281{col 50}{space 2} .3875331{col 61}{space 1}    1.09{col 70}{space 3}0.276{col 78}{space 4} .7803887{col 91}{space 3} 2.379906
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2891603{col 50}{space 2} .1442222{col 61}{space 1}   -2.49{col 70}{space 3}0.013{col 78}{space 4} .1087581{col 91}{space 3} .7688037
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 3: Worsen v Improve
. svy: logit import_directionworse i.employsector5 if infullmodel_TableD3 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       533
{txt}{col 1}Number of PSUs{col 20}= {res}      533{txt}{col 49}Population size{col 67}={res} 592.486062
{txt}{col 49}Design df{col 67}= {res}       532
{txt}{col 49}F({res}   4{txt},{res}    529{txt}){col 67}= {res}      0.57
{txt}{col 49}Prob > F{col 67}= {res}    0.6841

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}               import_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .8330113{col 50}{space 2} .2022967{col 61}{space 1}   -0.75{col 70}{space 3}0.452{col 78}{space 4}   .51697{col 91}{space 3}  1.34226
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .8709537{col 50}{space 2} .3931362{col 61}{space 1}   -0.31{col 70}{space 3}0.760{col 78}{space 4} .3588381{col 91}{space 3} 2.113935
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.217801{col 50}{space 2} .4563758{col 61}{space 1}    0.53{col 70}{space 3}0.599{col 78}{space 4} .5832539{col 91}{space 3} 2.542699
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .4985858{col 50}{space 2} .3043979{col 61}{space 1}   -1.14{col 70}{space 3}0.255{col 78}{space 4} .1502717{col 91}{space 3} 1.654256
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2}  2.50779{col 50}{space 2} .3561601{col 61}{space 1}    6.47{col 70}{space 3}0.000{col 78}{space 4} 1.897259{col 91}{space 3} 3.314788
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit import_directionworse i.employsector5 $controls_demographics if infullmodel_TableD3 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       533
{txt}{col 1}Number of PSUs{col 20}= {res}      533{txt}{col 49}Population size{col 67}={res} 592.486062
{txt}{col 49}Design df{col 67}= {res}       532
{txt}{col 49}F({res}  17{txt},{res}    516{txt}){col 67}= {res}      2.28
{txt}{col 49}Prob > F{col 67}= {res}    0.0026

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}               import_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9391509{col 50}{space 2} .2415127{col 61}{space 1}   -0.24{col 70}{space 3}0.807{col 78}{space 4} .5666837{col 91}{space 3} 1.556432
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .8300581{col 50}{space 2} .3940198{col 61}{space 1}   -0.39{col 70}{space 3}0.695{col 78}{space 4} .3266862{col 91}{space 3} 2.109047
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.013492{col 50}{space 2} .4191468{col 61}{space 1}    0.03{col 70}{space 3}0.974{col 78}{space 4} .4497681{col 91}{space 3} 2.283767
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2}  .549621{col 50}{space 2} .3468396{col 61}{space 1}   -0.95{col 70}{space 3}0.343{col 78}{space 4} .1591057{col 91}{space 3} 1.898632
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 2.142296{col 50}{space 2} .5710227{col 61}{space 1}    2.86{col 70}{space 3}0.004{col 78}{space 4} 1.269044{col 91}{space 3} 3.616447
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 4.581218{col 50}{space 2} 1.514217{col 61}{space 1}    4.60{col 70}{space 3}0.000{col 78}{space 4} 2.393284{col 91}{space 3} 8.769355
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .7548422{col 50}{space 2} .1706943{col 61}{space 1}   -1.24{col 70}{space 3}0.214{col 78}{space 4} .4840977{col 91}{space 3} 1.177008
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.101412{col 50}{space 2} .2589913{col 61}{space 1}    0.41{col 70}{space 3}0.681{col 78}{space 4} .6939658{col 91}{space 3} 1.748082
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.737503{col 50}{space 2}  .510699{col 61}{space 1}    1.88{col 70}{space 3}0.061{col 78}{space 4} .9753579{col 91}{space 3} 3.095189
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.887767{col 50}{space 2} .6886964{col 61}{space 1}    1.74{col 70}{space 3}0.082{col 78}{space 4} .9219434{col 91}{space 3} 3.865381
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.645772{col 50}{space 2} .6414268{col 61}{space 1}    1.28{col 70}{space 3}0.202{col 78}{space 4}  .765357{col 91}{space 3} 3.538957
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.596431{col 50}{space 2} .5581033{col 61}{space 1}    1.34{col 70}{space 3}0.181{col 78}{space 4} .8033346{col 91}{space 3} 3.172517
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.797841{col 50}{space 2} .6174862{col 61}{space 1}    1.71{col 70}{space 3}0.088{col 78}{space 4}  .915653{col 91}{space 3} 3.529973
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .9201556{col 50}{space 2} .2876195{col 61}{space 1}   -0.27{col 70}{space 3}0.790{col 78}{space 4} .4979533{col 91}{space 3} 1.700333
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.676232{col 50}{space 2} .6010057{col 61}{space 1}    1.44{col 70}{space 3}0.150{col 78}{space 4} .8287885{col 91}{space 3} 3.390195
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.546733{col 50}{space 2} .5325191{col 61}{space 1}    1.27{col 70}{space 3}0.206{col 78}{space 4}  .786484{col 91}{space 3}  3.04187
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.556283{col 50}{space 2} .5677038{col 61}{space 1}    1.21{col 70}{space 3}0.226{col 78}{space 4} .7601124{col 91}{space 3} 3.186394
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .5793447{col 50}{space 2} .2391191{col 61}{space 1}   -1.32{col 70}{space 3}0.187{col 78}{space 4} .2575198{col 91}{space 3} 1.303357
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit import_directionworse i.employsector5 $controls_demographics $controls_objective if infullmodel_TableD3 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       533
{txt}{col 1}Number of PSUs{col 20}= {res}      533{txt}{col 49}Population size{col 67}={res} 592.486062
{txt}{col 49}Design df{col 67}= {res}       532
{txt}{col 49}F({res}  23{txt},{res}    510{txt}){col 67}= {res}      1.76
{txt}{col 49}Prob > F{col 67}= {res}    0.0165

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}               import_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9074859{col 50}{space 2} .2415928{col 61}{space 1}   -0.36{col 70}{space 3}0.716{col 78}{space 4}  .537916{col 91}{space 3} 1.530965
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .7555217{col 50}{space 2} .3661597{col 61}{space 1}   -0.58{col 70}{space 3}0.563{col 78}{space 4} .2915922{col 91}{space 3} 1.957573
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2}   1.0286{col 50}{space 2} .4404442{col 61}{space 1}    0.07{col 70}{space 3}0.948{col 78}{space 4} .4435402{col 91}{space 3} 2.385395
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5997226{col 50}{space 2} .3572605{col 61}{space 1}   -0.86{col 70}{space 3}0.391{col 78}{space 4}  .186091{col 91}{space 3} 1.932749
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 2.313153{col 50}{space 2} .6313656{col 61}{space 1}    3.07{col 70}{space 3}0.002{col 78}{space 4}  1.35314{col 91}{space 3} 3.954268
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 4.734086{col 50}{space 2} 1.610438{col 61}{space 1}    4.57{col 70}{space 3}0.000{col 78}{space 4} 2.426693{col 91}{space 3} 9.235438
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .7399686{col 50}{space 2} .1671828{col 61}{space 1}   -1.33{col 70}{space 3}0.183{col 78}{space 4} .4747455{col 91}{space 3} 1.153362
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.080401{col 50}{space 2} .2550306{col 61}{space 1}    0.33{col 70}{space 3}0.743{col 78}{space 4} .6795148{col 91}{space 3} 1.717793
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.744612{col 50}{space 2} .5307557{col 61}{space 1}    1.83{col 70}{space 3}0.068{col 78}{space 4} .9597347{col 91}{space 3} 3.171368
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.779749{col 50}{space 2} .6540373{col 61}{space 1}    1.57{col 70}{space 3}0.117{col 78}{space 4} .8646471{col 91}{space 3} 3.663354
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.501792{col 50}{space 2} .5975836{col 61}{space 1}    1.02{col 70}{space 3}0.307{col 78}{space 4} .6872787{col 91}{space 3} 3.281609
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.504137{col 50}{space 2} .5283807{col 61}{space 1}    1.16{col 70}{space 3}0.246{col 78}{space 4} .7543821{col 91}{space 3} 2.999049
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.719397{col 50}{space 2} .5994877{col 61}{space 1}    1.55{col 70}{space 3}0.121{col 78}{space 4} .8667983{col 91}{space 3} 3.410626
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.015425{col 50}{space 2} .3558767{col 61}{space 1}    0.04{col 70}{space 3}0.965{col 78}{space 4} .5100897{col 91}{space 3} 2.021386
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.325183{col 50}{space 2} .6481626{col 61}{space 1}    0.58{col 70}{space 3}0.565{col 78}{space 4} .5069834{col 91}{space 3} 3.463841
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.595465{col 50}{space 2} .7151305{col 61}{space 1}    1.04{col 70}{space 3}0.298{col 78}{space 4} .6614326{col 91}{space 3} 3.848476
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.431199{col 50}{space 2} .8129854{col 61}{space 1}    0.63{col 70}{space 3}0.528{col 78}{space 4} .4688963{col 91}{space 3} 4.368409
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .8610504{col 50}{space 2}  .276007{col 61}{space 1}   -0.47{col 70}{space 3}0.641{col 78}{space 4} .4587292{col 91}{space 3} 1.616221
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.740808{col 50}{space 2} .9973024{col 61}{space 1}    0.97{col 70}{space 3}0.334{col 78}{space 4} .5649221{col 91}{space 3}   5.3643
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.508786{col 50}{space 2} .8598458{col 61}{space 1}    0.72{col 70}{space 3}0.471{col 78}{space 4} .4925246{col 91}{space 3}  4.62197
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} 1.317195{col 50}{space 2} .7961797{col 61}{space 1}    0.46{col 70}{space 3}0.649{col 78}{space 4} .4017605{col 91}{space 3} 4.318499
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .9289508{col 50}{space 2} .3635688{col 61}{space 1}   -0.19{col 70}{space 3}0.851{col 78}{space 4} .4306193{col 91}{space 3} 2.003973
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} .6042082{col 50}{space 2} .2071826{col 61}{space 1}   -1.47{col 70}{space 3}0.142{col 78}{space 4} .3080666{col 91}{space 3} 1.185028
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .5285986{col 50}{space 2} .2792063{col 61}{space 1}   -1.21{col 70}{space 3}0.228{col 78}{space 4} .1872817{col 91}{space 3} 1.491958
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. 
. ******* Equivalised HH Income ********
. 
. 
. * Model 1: Worsen 
. svy: logit import_worsen i.equivincomeHHquintile_HBAI_imp if infullmodel_TableD3 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   4{txt},{res}   3959{txt}){col 67}= {res}      3.47
{txt}{col 49}Prob > F{col 67}= {res}    0.0078

{txt}{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 32}{c |}{col 44}  Linearized
{col 1}                 import_worsen{col 32}{c |} Odds Ratio{col 44}   Std. Err.{col 56}      t{col 64}   P>|t|{col 72}     [95% Con{col 85}f. Interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
equivincomeHHquintile_HBAI_imp {c |}
{space 28}2  {c |}{col 32}{res}{space 2} .8557076{col 44}{space 2} .1807791{col 55}{space 1}   -0.74{col 64}{space 3}0.461{col 72}{space 4} .5655139{col 85}{space 3} 1.294814
{txt}{space 28}3  {c |}{col 32}{res}{space 2} .6255533{col 44}{space 2} .1286283{col 55}{space 1}   -2.28{col 64}{space 3}0.023{col 72}{space 4} .4180075{col 85}{space 3} .9361482
{txt}{space 28}4  {c |}{col 32}{res}{space 2} .7284826{col 44}{space 2} .1378975{col 55}{space 1}   -1.67{col 64}{space 3}0.094{col 72}{space 4} .5026232{col 85}{space 3} 1.055834
{txt}{space 17}Top Quintile  {c |}{col 32}{res}{space 2} .5293869{col 44}{space 2} .1006824{col 55}{space 1}   -3.34{col 64}{space 3}0.001{col 72}{space 4} .3646166{col 85}{space 3} .7686168
{txt}{space 30} {c |}
{space 25}_cons {c |}{col 32}{res}{space 2} .1631431{col 44}{space 2} .0243967{col 55}{space 1}  -12.12{col 64}{space 3}0.000{col 72}{space 4} .1216857{col 85}{space 3} .2187247
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit import_worsen i.equivincomeHHquintile_HBAI_imp $controls_demographics if infullmodel_TableD3 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      4.00
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                       import_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}  .879634{col 50}{space 2} .1907448{col 61}{space 1}   -0.59{col 70}{space 3}0.554{col 78}{space 4} .5749979{col 91}{space 3} 1.345668
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .6385776{col 50}{space 2} .1376237{col 61}{space 1}   -2.08{col 70}{space 3}0.037{col 78}{space 4} .4185141{col 91}{space 3} .9743552
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .7576182{col 50}{space 2} .1553592{col 61}{space 1}   -1.35{col 70}{space 3}0.176{col 78}{space 4} .5068127{col 91}{space 3}  1.13254
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .5546876{col 50}{space 2} .1239106{col 61}{space 1}   -2.64{col 70}{space 3}0.008{col 78}{space 4} .3579663{col 91}{space 3} .8595177
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.132824{col 50}{space 2} .2138252{col 61}{space 1}    0.66{col 70}{space 3}0.509{col 78}{space 4} .7824304{col 91}{space 3} 1.640134
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.085446{col 50}{space 2} .2166108{col 61}{space 1}    0.41{col 70}{space 3}0.681{col 78}{space 4} .7339914{col 91}{space 3} 1.605185
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6543863{col 50}{space 2} .0837702{col 61}{space 1}   -3.31{col 70}{space 3}0.001{col 78}{space 4} .5091382{col 91}{space 3} .8410709
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.113073{col 50}{space 2} .1462693{col 61}{space 1}    0.82{col 70}{space 3}0.415{col 78}{space 4} .8602666{col 91}{space 3} 1.440173
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .8912295{col 50}{space 2} .1472665{col 61}{space 1}   -0.70{col 70}{space 3}0.486{col 78}{space 4} .6446058{col 91}{space 3}  1.23221
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .5988927{col 50}{space 2} .0880808{col 61}{space 1}   -3.49{col 70}{space 3}0.000{col 78}{space 4} .4488717{col 91}{space 3} .7990534
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .8414512{col 50}{space 2}  .223756{col 61}{space 1}   -0.65{col 70}{space 3}0.516{col 78}{space 4} .4995868{col 91}{space 3} 1.417251
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.056759{col 50}{space 2} .2076127{col 61}{space 1}    0.28{col 70}{space 3}0.779{col 78}{space 4}  .718946{col 91}{space 3} 1.553301
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5350543{col 50}{space 2} .2184544{col 61}{space 1}   -1.53{col 70}{space 3}0.126{col 78}{space 4} .2403031{col 91}{space 3} 1.191342
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.255098{col 50}{space 2} .2202454{col 61}{space 1}    1.29{col 70}{space 3}0.195{col 78}{space 4} .8897394{col 91}{space 3} 1.770487
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.174341{col 50}{space 2} .2245627{col 61}{space 1}    0.84{col 70}{space 3}0.401{col 78}{space 4} .8071867{col 91}{space 3} 1.708498
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.295484{col 50}{space 2} .2566914{col 61}{space 1}    1.31{col 70}{space 3}0.191{col 78}{space 4} .8784575{col 91}{space 3} 1.910484
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.729091{col 50}{space 2} .3422669{col 61}{space 1}    2.77{col 70}{space 3}0.006{col 78}{space 4} 1.172936{col 91}{space 3} 2.548951
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .1640278{col 50}{space 2} .0453379{col 61}{space 1}   -6.54{col 70}{space 3}0.000{col 78}{space 4} .0954048{col 91}{space 3}   .28201
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit import_worsen i.equivincomeHHquintile_HBAI_imp $controls_demographics $controls_objective if infullmodel_TableD3 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      3.43
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                       import_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .8804051{col 50}{space 2} .1915187{col 61}{space 1}   -0.59{col 70}{space 3}0.558{col 78}{space 4} .5747249{col 91}{space 3} 1.348668
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .6408041{col 50}{space 2} .1391445{col 61}{space 1}   -2.05{col 70}{space 3}0.040{col 78}{space 4} .4186379{col 91}{space 3} .9808713
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .7738245{col 50}{space 2} .1608112{col 61}{space 1}   -1.23{col 70}{space 3}0.217{col 78}{space 4} .5148696{col 91}{space 3} 1.163022
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .5645058{col 50}{space 2} .1274512{col 61}{space 1}   -2.53{col 70}{space 3}0.011{col 78}{space 4} .3626017{col 91}{space 3} .8788345
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2}  1.14489{col 50}{space 2} .2193418{col 61}{space 1}    0.71{col 70}{space 3}0.480{col 78}{space 4} .7863899{col 91}{space 3} 1.666824
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.091017{col 50}{space 2} .2200251{col 61}{space 1}    0.43{col 70}{space 3}0.666{col 78}{space 4} .7347125{col 91}{space 3} 1.620114
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}  .652949{col 50}{space 2} .0838384{col 61}{space 1}   -3.32{col 70}{space 3}0.001{col 78}{space 4} .5076354{col 91}{space 3} .8398595
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.106137{col 50}{space 2} .1468444{col 61}{space 1}    0.76{col 70}{space 3}0.447{col 78}{space 4}  .852656{col 91}{space 3} 1.434974
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .8677224{col 50}{space 2} .1436729{col 61}{space 1}   -0.86{col 70}{space 3}0.392{col 78}{space 4} .6271918{col 91}{space 3} 1.200498
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .5892368{col 50}{space 2} .0900676{col 61}{space 1}   -3.46{col 70}{space 3}0.001{col 78}{space 4} .4366566{col 91}{space 3} .7951329
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}  .849199{col 50}{space 2} .2281017{col 61}{space 1}   -0.61{col 70}{space 3}0.543{col 78}{space 4} .5015336{col 91}{space 3} 1.437868
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.072952{col 50}{space 2} .2137632{col 61}{space 1}    0.35{col 70}{space 3}0.724{col 78}{space 4} .7260127{col 91}{space 3} 1.585682
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5238108{col 50}{space 2} .2125478{col 61}{space 1}   -1.59{col 70}{space 3}0.111{col 78}{space 4}  .236415{col 91}{space 3} 1.160577
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.240085{col 50}{space 2} .2334789{col 61}{space 1}    1.14{col 70}{space 3}0.253{col 78}{space 4} .8573168{col 91}{space 3} 1.793749
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.238847{col 50}{space 2} .3908825{col 61}{space 1}    0.68{col 70}{space 3}0.497{col 78}{space 4} .6673647{col 91}{space 3} 2.299703
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.287317{col 50}{space 2} .2907146{col 61}{space 1}    1.12{col 70}{space 3}0.263{col 78}{space 4} .8267987{col 91}{space 3} 2.004338
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 2.010093{col 50}{space 2} .5449394{col 61}{space 1}    2.58{col 70}{space 3}0.010{col 78}{space 4} 1.181364{col 91}{space 3} 3.420178
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .7500211{col 50}{space 2} .1116783{col 61}{space 1}   -1.93{col 70}{space 3}0.053{col 78}{space 4} .5601323{col 91}{space 3} 1.004284
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 2.376303{col 50}{space 2} .7231256{col 61}{space 1}    2.84{col 70}{space 3}0.004{col 78}{space 4} 1.308567{col 91}{space 3} 4.315265
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 2.171363{col 50}{space 2} .6616116{col 61}{space 1}    2.54{col 70}{space 3}0.011{col 78}{space 4} 1.194794{col 91}{space 3} 3.946133
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .8999696{col 50}{space 2} .3310424{col 61}{space 1}   -0.29{col 70}{space 3}0.774{col 78}{space 4} .4375508{col 91}{space 3} 1.851088
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.086634{col 50}{space 2} .2196671{col 61}{space 1}    0.41{col 70}{space 3}0.681{col 78}{space 4}  .731067{col 91}{space 3} 1.615137
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} .9521414{col 50}{space 2} .1698551{col 61}{space 1}   -0.27{col 70}{space 3}0.783{col 78}{space 4} .6711304{col 91}{space 3} 1.350815
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .0978468{col 50}{space 2} .0364748{col 61}{space 1}   -6.24{col 70}{space 3}0.000{col 78}{space 4} .0471133{col 91}{space 3} .2032123
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 2: Improve 
. svy: logit import_improve i.equivincomeHHquintile_HBAI_imp if infullmodel_TableD3 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   4{txt},{res}   3959{txt}){col 67}= {res}      3.86
{txt}{col 49}Prob > F{col 67}= {res}    0.0039

{txt}{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 32}{c |}{col 44}  Linearized
{col 1}                import_improve{col 32}{c |} Odds Ratio{col 44}   Std. Err.{col 56}      t{col 64}   P>|t|{col 72}     [95% Con{col 85}f. Interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
equivincomeHHquintile_HBAI_imp {c |}
{space 28}2  {c |}{col 32}{res}{space 2} .3820401{col 44}{space 2} .1175167{col 55}{space 1}   -3.13{col 64}{space 3}0.002{col 72}{space 4} .2090243{col 85}{space 3} .6982666
{txt}{space 28}3  {c |}{col 32}{res}{space 2}  .417736{col 44}{space 2} .1294659{col 55}{space 1}   -2.82{col 64}{space 3}0.005{col 72}{space 4} .2275174{col 85}{space 3} .7669891
{txt}{space 28}4  {c |}{col 32}{res}{space 2} .5112459{col 44}{space 2}   .13976{col 55}{space 1}   -2.45{col 64}{space 3}0.014{col 72}{space 4} .2991332{col 85}{space 3}  .873766
{txt}{space 17}Top Quintile  {c |}{col 32}{res}{space 2} .3979985{col 44}{space 2} .1048838{col 55}{space 1}   -3.50{col 64}{space 3}0.000{col 72}{space 4} .2374091{col 85}{space 3} .6672144
{txt}{space 30} {c |}
{space 25}_cons {c |}{col 32}{res}{space 2} .0898483{col 44}{space 2} .0182552{col 55}{space 1}  -11.86{col 64}{space 3}0.000{col 72}{space 4} .0603269{col 85}{space 3} .1338162
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit import_improve i.equivincomeHHquintile_HBAI_imp $controls_demographics if infullmodel_TableD3 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      3.53
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                      import_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .3727353{col 50}{space 2}  .115085{col 61}{space 1}   -3.20{col 70}{space 3}0.001{col 78}{space 4} .2034721{col 91}{space 3}  .682804
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .3385937{col 50}{space 2}  .109652{col 61}{space 1}   -3.34{col 70}{space 3}0.001{col 78}{space 4} .1794474{col 91}{space 3}  .638882
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .4079394{col 50}{space 2} .1267116{col 61}{space 1}   -2.89{col 70}{space 3}0.004{col 78}{space 4}  .221881{col 91}{space 3} .7500174
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .2649332{col 50}{space 2} .0865925{col 61}{space 1}   -4.06{col 70}{space 3}0.000{col 78}{space 4} .1395851{col 91}{space 3} .5028447
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .4813711{col 50}{space 2} .0989775{col 61}{space 1}   -3.56{col 70}{space 3}0.000{col 78}{space 4} .3216666{col 91}{space 3} .7203673
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .2059369{col 50}{space 2} .0579086{col 61}{space 1}   -5.62{col 70}{space 3}0.000{col 78}{space 4} .1186607{col 91}{space 3} .3574057
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}  .819794{col 50}{space 2}  .148332{col 61}{space 1}   -1.10{col 70}{space 3}0.272{col 78}{space 4} .5749669{col 91}{space 3} 1.168871
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.022786{col 50}{space 2} .2006774{col 61}{space 1}    0.11{col 70}{space 3}0.909{col 78}{space 4} .6961809{col 91}{space 3} 1.502613
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .5329506{col 50}{space 2} .1449172{col 61}{space 1}   -2.31{col 70}{space 3}0.021{col 78}{space 4} .3127245{col 91}{space 3} .9082639
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .7019361{col 50}{space 2} .1437957{col 61}{space 1}   -1.73{col 70}{space 3}0.084{col 78}{space 4} .4697542{col 91}{space 3} 1.048877
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9356342{col 50}{space 2} .3569388{col 61}{space 1}   -0.17{col 70}{space 3}0.862{col 78}{space 4} .4428725{col 91}{space 3} 1.976667
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.143594{col 50}{space 2} .4221313{col 61}{space 1}    0.36{col 70}{space 3}0.716{col 78}{space 4} .5545935{col 91}{space 3} 2.358138
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.157162{col 50}{space 2} .5955343{col 61}{space 1}    0.28{col 70}{space 3}0.777{col 78}{space 4} .4218802{col 91}{space 3} 3.173946
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.498316{col 50}{space 2} .3613864{col 61}{space 1}    1.68{col 70}{space 3}0.094{col 78}{space 4} .9337597{col 91}{space 3} 2.404207
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}  .718643{col 50}{space 2} .2206643{col 61}{space 1}   -1.08{col 70}{space 3}0.282{col 78}{space 4} .3936099{col 91}{space 3}  1.31208
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .7920011{col 50}{space 2}  .249908{col 61}{space 1}   -0.74{col 70}{space 3}0.460{col 78}{space 4} .4266342{col 91}{space 3} 1.470266
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .9644461{col 50}{space 2} .3338255{col 61}{space 1}   -0.10{col 70}{space 3}0.917{col 78}{space 4} .4892828{col 91}{space 3} 1.901061
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .3241045{col 50}{space 2} .1221441{col 61}{space 1}   -2.99{col 70}{space 3}0.003{col 78}{space 4} .1548095{col 91}{space 3} .6785353
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit import_improve i.equivincomeHHquintile_HBAI_imp $controls_demographics $controls_objective if infullmodel_TableD3 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      2.81
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                      import_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .3777598{col 50}{space 2} .1154978{col 61}{space 1}   -3.18{col 70}{space 3}0.001{col 78}{space 4} .2074371{col 91}{space 3} .6879312
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .3441002{col 50}{space 2} .1114877{col 61}{space 1}   -3.29{col 70}{space 3}0.001{col 78}{space 4} .1823111{col 91}{space 3} .6494664
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .4077277{col 50}{space 2} .1272669{col 61}{space 1}   -2.87{col 70}{space 3}0.004{col 78}{space 4} .2211045{col 91}{space 3} .7518703
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .2705826{col 50}{space 2} .0887859{col 61}{space 1}   -3.98{col 70}{space 3}0.000{col 78}{space 4} .1422037{col 91}{space 3} .5148598
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .4859691{col 50}{space 2}  .100689{col 61}{space 1}   -3.48{col 70}{space 3}0.001{col 78}{space 4} .3237371{col 91}{space 3} .7294992
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .2047739{col 50}{space 2} .0580106{col 61}{space 1}   -5.60{col 70}{space 3}0.000{col 78}{space 4} .1175069{col 91}{space 3} .3568502
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .8222797{col 50}{space 2} .1492892{col 61}{space 1}   -1.08{col 70}{space 3}0.281{col 78}{space 4} .5760129{col 91}{space 3} 1.173835
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.031034{col 50}{space 2} .2059519{col 61}{space 1}    0.15{col 70}{space 3}0.878{col 78}{space 4} .6969337{col 91}{space 3} 1.525299
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2}  .532034{col 50}{space 2} .1453596{col 61}{space 1}   -2.31{col 70}{space 3}0.021{col 78}{space 4}  .311392{col 91}{space 3} .9090155
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .6794348{col 50}{space 2} .1425353{col 61}{space 1}   -1.84{col 70}{space 3}0.066{col 78}{space 4} .4503227{col 91}{space 3} 1.025113
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9105414{col 50}{space 2} .3478142{col 61}{space 1}   -0.25{col 70}{space 3}0.806{col 78}{space 4} .4305793{col 91}{space 3} 1.925512
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.146478{col 50}{space 2} .4223236{col 61}{space 1}    0.37{col 70}{space 3}0.711{col 78}{space 4} .5568215{col 91}{space 3} 2.360561
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.159751{col 50}{space 2} .5901265{col 61}{space 1}    0.29{col 70}{space 3}0.771{col 78}{space 4} .4276692{col 91}{space 3} 3.145007
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.453146{col 50}{space 2} .4086095{col 61}{space 1}    1.33{col 70}{space 3}0.184{col 78}{space 4} .8373113{col 91}{space 3}  2.52192
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.159795{col 50}{space 2}  .515801{col 61}{space 1}    0.33{col 70}{space 3}0.739{col 78}{space 4} .4849612{col 91}{space 3} 2.773676
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .7782851{col 50}{space 2} .3033448{col 61}{space 1}   -0.64{col 70}{space 3}0.520{col 78}{space 4} .3624704{col 91}{space 3} 1.671109
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .9747707{col 50}{space 2} .5281169{col 61}{space 1}   -0.05{col 70}{space 3}0.962{col 78}{space 4} .3369711{col 91}{space 3} 2.819761
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .9232136{col 50}{space 2} .2310483{col 61}{space 1}   -0.32{col 70}{space 3}0.750{col 78}{space 4} .5652115{col 91}{space 3} 1.507973
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} .9426035{col 50}{space 2} .4463135{col 61}{space 1}   -0.12{col 70}{space 3}0.901{col 78}{space 4} .3725377{col 91}{space 3} 2.384997
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.144866{col 50}{space 2} .5556917{col 61}{space 1}    0.28{col 70}{space 3}0.780{col 78}{space 4} .4420531{col 91}{space 3} 2.965068
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .5611062{col 50}{space 2} .3015494{col 61}{space 1}   -1.08{col 70}{space 3}0.282{col 78}{space 4} .1956379{col 91}{space 3} 1.609301
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.090376{col 50}{space 2} .3555053{col 61}{space 1}    0.27{col 70}{space 3}0.791{col 78}{space 4} .5753957{col 91}{space 3} 2.066266
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2}  1.36281{col 50}{space 2} .3875331{col 61}{space 1}    1.09{col 70}{space 3}0.276{col 78}{space 4} .7803887{col 91}{space 3} 2.379906
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2891603{col 50}{space 2} .1442222{col 61}{space 1}   -2.49{col 70}{space 3}0.013{col 78}{space 4} .1087581{col 91}{space 3} .7688037
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 3: Worsen v Improve
. svy: logit import_directionworse i.equivincomeHHquintile_HBAI_imp if infullmodel_TableD3 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       533
{txt}{col 1}Number of PSUs{col 20}= {res}      533{txt}{col 49}Population size{col 67}={res} 592.486062
{txt}{col 49}Design df{col 67}= {res}       532
{txt}{col 49}F({res}   4{txt},{res}    529{txt}){col 67}= {res}      1.18
{txt}{col 49}Prob > F{col 67}= {res}    0.3184

{txt}{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 32}{c |}{col 44}  Linearized
{col 1}         import_directionworse{col 32}{c |} Odds Ratio{col 44}   Std. Err.{col 56}      t{col 64}   P>|t|{col 72}     [95% Con{col 85}f. Interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
equivincomeHHquintile_HBAI_imp {c |}
{space 28}2  {c |}{col 32}{res}{space 2} 2.169639{col 44}{space 2} .7796299{col 55}{space 1}    2.16{col 64}{space 3}0.032{col 72}{space 4} 1.071081{col 85}{space 3} 4.394935
{txt}{space 28}3  {c |}{col 32}{res}{space 2} 1.504625{col 44}{space 2} .5402239{col 55}{space 1}    1.14{col 64}{space 3}0.256{col 72}{space 4} .7432147{col 85}{space 3} 3.046088
{txt}{space 28}4  {c |}{col 32}{res}{space 2} 1.421643{col 44}{space 2} .4541338{col 55}{space 1}    1.10{col 64}{space 3}0.271{col 72}{space 4} .7590318{col 85}{space 3} 2.662691
{txt}{space 17}Top Quintile  {c |}{col 32}{res}{space 2} 1.353449{col 44}{space 2} .4231249{col 55}{space 1}    0.97{col 64}{space 3}0.333{col 72}{space 4} .7323629{col 85}{space 3} 2.501251
{txt}{space 30} {c |}
{space 25}_cons {c |}{col 32}{res}{space 2} 1.701342{col 44}{space 2} .4086795{col 55}{space 1}    2.21{col 64}{space 3}0.027{col 72}{space 4} 1.061349{col 85}{space 3} 2.727251
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit import_directionworse i.equivincomeHHquintile_HBAI_imp $controls_demographics if infullmodel_TableD3 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       533
{txt}{col 1}Number of PSUs{col 20}= {res}      533{txt}{col 49}Population size{col 67}={res} 592.486062
{txt}{col 49}Design df{col 67}= {res}       532
{txt}{col 49}F({res}  17{txt},{res}    516{txt}){col 67}= {res}      2.28
{txt}{col 49}Prob > F{col 67}= {res}    0.0026

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}               import_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.887767{col 50}{space 2} .6886964{col 61}{space 1}    1.74{col 70}{space 3}0.082{col 78}{space 4} .9219434{col 91}{space 3} 3.865381
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.645772{col 50}{space 2} .6414268{col 61}{space 1}    1.28{col 70}{space 3}0.202{col 78}{space 4}  .765357{col 91}{space 3} 3.538957
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.596431{col 50}{space 2} .5581033{col 61}{space 1}    1.34{col 70}{space 3}0.181{col 78}{space 4} .8033346{col 91}{space 3} 3.172517
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.797841{col 50}{space 2} .6174862{col 61}{space 1}    1.71{col 70}{space 3}0.088{col 78}{space 4}  .915653{col 91}{space 3} 3.529973
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 2.142296{col 50}{space 2} .5710227{col 61}{space 1}    2.86{col 70}{space 3}0.004{col 78}{space 4} 1.269044{col 91}{space 3} 3.616447
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 4.581218{col 50}{space 2} 1.514217{col 61}{space 1}    4.60{col 70}{space 3}0.000{col 78}{space 4} 2.393284{col 91}{space 3} 8.769355
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .7548422{col 50}{space 2} .1706943{col 61}{space 1}   -1.24{col 70}{space 3}0.214{col 78}{space 4} .4840977{col 91}{space 3} 1.177008
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.101412{col 50}{space 2} .2589913{col 61}{space 1}    0.41{col 70}{space 3}0.681{col 78}{space 4} .6939658{col 91}{space 3} 1.748082
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.737503{col 50}{space 2}  .510699{col 61}{space 1}    1.88{col 70}{space 3}0.061{col 78}{space 4} .9753579{col 91}{space 3} 3.095189
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9391509{col 50}{space 2} .2415127{col 61}{space 1}   -0.24{col 70}{space 3}0.807{col 78}{space 4} .5666837{col 91}{space 3} 1.556432
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .8300581{col 50}{space 2} .3940198{col 61}{space 1}   -0.39{col 70}{space 3}0.695{col 78}{space 4} .3266862{col 91}{space 3} 2.109047
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.013492{col 50}{space 2} .4191468{col 61}{space 1}    0.03{col 70}{space 3}0.974{col 78}{space 4} .4497681{col 91}{space 3} 2.283767
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2}  .549621{col 50}{space 2} .3468396{col 61}{space 1}   -0.95{col 70}{space 3}0.343{col 78}{space 4} .1591057{col 91}{space 3} 1.898632
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .9201556{col 50}{space 2} .2876195{col 61}{space 1}   -0.27{col 70}{space 3}0.790{col 78}{space 4} .4979533{col 91}{space 3} 1.700333
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.676232{col 50}{space 2} .6010057{col 61}{space 1}    1.44{col 70}{space 3}0.150{col 78}{space 4} .8287885{col 91}{space 3} 3.390195
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.546733{col 50}{space 2} .5325191{col 61}{space 1}    1.27{col 70}{space 3}0.206{col 78}{space 4}  .786484{col 91}{space 3}  3.04187
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.556283{col 50}{space 2} .5677038{col 61}{space 1}    1.21{col 70}{space 3}0.226{col 78}{space 4} .7601124{col 91}{space 3} 3.186394
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .5793447{col 50}{space 2} .2391191{col 61}{space 1}   -1.32{col 70}{space 3}0.187{col 78}{space 4} .2575198{col 91}{space 3} 1.303357
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit import_directionworse i.equivincomeHHquintile_HBAI_imp $controls_demographics $controls_objective if infullmodel_TableD3 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       533
{txt}{col 1}Number of PSUs{col 20}= {res}      533{txt}{col 49}Population size{col 67}={res} 592.486062
{txt}{col 49}Design df{col 67}= {res}       532
{txt}{col 49}F({res}  23{txt},{res}    510{txt}){col 67}= {res}      1.76
{txt}{col 49}Prob > F{col 67}= {res}    0.0165

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}               import_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.779749{col 50}{space 2} .6540373{col 61}{space 1}    1.57{col 70}{space 3}0.117{col 78}{space 4} .8646471{col 91}{space 3} 3.663354
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.501792{col 50}{space 2} .5975836{col 61}{space 1}    1.02{col 70}{space 3}0.307{col 78}{space 4} .6872787{col 91}{space 3} 3.281609
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.504137{col 50}{space 2} .5283807{col 61}{space 1}    1.16{col 70}{space 3}0.246{col 78}{space 4} .7543821{col 91}{space 3} 2.999049
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.719397{col 50}{space 2} .5994877{col 61}{space 1}    1.55{col 70}{space 3}0.121{col 78}{space 4} .8667983{col 91}{space 3} 3.410626
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 2.313153{col 50}{space 2} .6313656{col 61}{space 1}    3.07{col 70}{space 3}0.002{col 78}{space 4}  1.35314{col 91}{space 3} 3.954268
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 4.734086{col 50}{space 2} 1.610438{col 61}{space 1}    4.57{col 70}{space 3}0.000{col 78}{space 4} 2.426693{col 91}{space 3} 9.235438
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .7399686{col 50}{space 2} .1671828{col 61}{space 1}   -1.33{col 70}{space 3}0.183{col 78}{space 4} .4747455{col 91}{space 3} 1.153362
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.080401{col 50}{space 2} .2550306{col 61}{space 1}    0.33{col 70}{space 3}0.743{col 78}{space 4} .6795148{col 91}{space 3} 1.717793
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.744612{col 50}{space 2} .5307557{col 61}{space 1}    1.83{col 70}{space 3}0.068{col 78}{space 4} .9597347{col 91}{space 3} 3.171368
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9074859{col 50}{space 2} .2415928{col 61}{space 1}   -0.36{col 70}{space 3}0.716{col 78}{space 4}  .537916{col 91}{space 3} 1.530965
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .7555217{col 50}{space 2} .3661597{col 61}{space 1}   -0.58{col 70}{space 3}0.563{col 78}{space 4} .2915922{col 91}{space 3} 1.957573
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2}   1.0286{col 50}{space 2} .4404442{col 61}{space 1}    0.07{col 70}{space 3}0.948{col 78}{space 4} .4435402{col 91}{space 3} 2.385395
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5997226{col 50}{space 2} .3572605{col 61}{space 1}   -0.86{col 70}{space 3}0.391{col 78}{space 4}  .186091{col 91}{space 3} 1.932749
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.015425{col 50}{space 2} .3558767{col 61}{space 1}    0.04{col 70}{space 3}0.965{col 78}{space 4} .5100897{col 91}{space 3} 2.021386
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.325183{col 50}{space 2} .6481626{col 61}{space 1}    0.58{col 70}{space 3}0.565{col 78}{space 4} .5069834{col 91}{space 3} 3.463841
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.595465{col 50}{space 2} .7151305{col 61}{space 1}    1.04{col 70}{space 3}0.298{col 78}{space 4} .6614326{col 91}{space 3} 3.848476
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.431199{col 50}{space 2} .8129854{col 61}{space 1}    0.63{col 70}{space 3}0.528{col 78}{space 4} .4688963{col 91}{space 3} 4.368409
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .8610504{col 50}{space 2}  .276007{col 61}{space 1}   -0.47{col 70}{space 3}0.641{col 78}{space 4} .4587292{col 91}{space 3} 1.616221
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.740808{col 50}{space 2} .9973024{col 61}{space 1}    0.97{col 70}{space 3}0.334{col 78}{space 4} .5649221{col 91}{space 3}   5.3643
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.508786{col 50}{space 2} .8598458{col 61}{space 1}    0.72{col 70}{space 3}0.471{col 78}{space 4} .4925246{col 91}{space 3}  4.62197
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} 1.317195{col 50}{space 2} .7961797{col 61}{space 1}    0.46{col 70}{space 3}0.649{col 78}{space 4} .4017605{col 91}{space 3} 4.318499
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .9289508{col 50}{space 2} .3635688{col 61}{space 1}   -0.19{col 70}{space 3}0.851{col 78}{space 4} .4306193{col 91}{space 3} 2.003973
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} .6042082{col 50}{space 2} .2071826{col 61}{space 1}   -1.47{col 70}{space 3}0.142{col 78}{space 4} .3080666{col 91}{space 3} 1.185028
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .5285986{col 50}{space 2} .2792063{col 61}{space 1}   -1.21{col 70}{space 3}0.228{col 78}{space 4} .1872817{col 91}{space 3} 1.491958
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. ******* Employment Status ********
. 
. 
. * Model 1: Worsen 
. svy: logit import_worsen ib2.employmentstatus5 if infullmodel_TableD3 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   1{txt},{res}   3962{txt}){col 67}= {res}      0.10
{txt}{col 49}Prob > F{col 67}= {res}    0.7556

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}  Linearized
{col 1}    import_worsen{col 19}{c |} Odds Ratio{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
employmentstatus5 {c |}
{space 7}FT Worker  {c |}{col 19}{res}{space 2} 1.044586{col 31}{space 2} .1464054{col 42}{space 1}    0.31{col 51}{space 3}0.756{col 59}{space 4}  .793609{col 72}{space 3} 1.374935
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} .1102751{col 31}{space 2} .0135508{col 42}{space 1}  -17.94{col 51}{space 3}0.000{col 59}{space 4}  .086666{col 72}{space 3} .1403158
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit import_worsen ib2.employmentstatus5 $controls_demographics if infullmodel_TableD3 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      4.00
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                       import_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} 1.122046{col 50}{space 2} .1854065{col 61}{space 1}    0.70{col 70}{space 3}0.486{col 78}{space 4} .8115498{col 91}{space 3} 1.551336
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.132824{col 50}{space 2} .2138252{col 61}{space 1}    0.66{col 70}{space 3}0.509{col 78}{space 4} .7824304{col 91}{space 3} 1.640134
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.085446{col 50}{space 2} .2166108{col 61}{space 1}    0.41{col 70}{space 3}0.681{col 78}{space 4} .7339914{col 91}{space 3} 1.605185
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6543863{col 50}{space 2} .0837702{col 61}{space 1}   -3.31{col 70}{space 3}0.001{col 78}{space 4} .5091382{col 91}{space 3} .8410709
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.113073{col 50}{space 2} .1462693{col 61}{space 1}    0.82{col 70}{space 3}0.415{col 78}{space 4} .8602666{col 91}{space 3} 1.440173
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .5988927{col 50}{space 2} .0880808{col 61}{space 1}   -3.49{col 70}{space 3}0.000{col 78}{space 4} .4488717{col 91}{space 3} .7990534
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .8414512{col 50}{space 2}  .223756{col 61}{space 1}   -0.65{col 70}{space 3}0.516{col 78}{space 4} .4995868{col 91}{space 3} 1.417251
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.056759{col 50}{space 2} .2076127{col 61}{space 1}    0.28{col 70}{space 3}0.779{col 78}{space 4}  .718946{col 91}{space 3} 1.553301
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5350543{col 50}{space 2} .2184544{col 61}{space 1}   -1.53{col 70}{space 3}0.126{col 78}{space 4} .2403031{col 91}{space 3} 1.191342
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}  .879634{col 50}{space 2} .1907448{col 61}{space 1}   -0.59{col 70}{space 3}0.554{col 78}{space 4} .5749979{col 91}{space 3} 1.345668
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .6385776{col 50}{space 2} .1376237{col 61}{space 1}   -2.08{col 70}{space 3}0.037{col 78}{space 4} .4185141{col 91}{space 3} .9743552
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .7576182{col 50}{space 2} .1553592{col 61}{space 1}   -1.35{col 70}{space 3}0.176{col 78}{space 4} .5068127{col 91}{space 3}  1.13254
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .5546876{col 50}{space 2} .1239106{col 61}{space 1}   -2.64{col 70}{space 3}0.008{col 78}{space 4} .3579663{col 91}{space 3} .8595177
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.255098{col 50}{space 2} .2202454{col 61}{space 1}    1.29{col 70}{space 3}0.195{col 78}{space 4} .8897394{col 91}{space 3} 1.770487
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.174341{col 50}{space 2} .2245627{col 61}{space 1}    0.84{col 70}{space 3}0.401{col 78}{space 4} .8071867{col 91}{space 3} 1.708498
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.295484{col 50}{space 2} .2566914{col 61}{space 1}    1.31{col 70}{space 3}0.191{col 78}{space 4} .8784575{col 91}{space 3} 1.910484
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.729091{col 50}{space 2} .3422669{col 61}{space 1}    2.77{col 70}{space 3}0.006{col 78}{space 4} 1.172936{col 91}{space 3} 2.548951
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .1461864{col 50}{space 2} .0470717{col 61}{space 1}   -5.97{col 70}{space 3}0.000{col 78}{space 4} .0777567{col 91}{space 3} .2748375
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit import_worsen ib2.employmentstatus5 $controls_demographics $controls_objective if infullmodel_TableD3 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      3.43
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                       import_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} 1.152442{col 50}{space 2} .1908152{col 61}{space 1}    0.86{col 70}{space 3}0.392{col 78}{space 4} .8329879{col 91}{space 3} 1.594409
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2}  1.14489{col 50}{space 2} .2193418{col 61}{space 1}    0.71{col 70}{space 3}0.480{col 78}{space 4} .7863899{col 91}{space 3} 1.666824
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.091017{col 50}{space 2} .2200251{col 61}{space 1}    0.43{col 70}{space 3}0.666{col 78}{space 4} .7347125{col 91}{space 3} 1.620114
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}  .652949{col 50}{space 2} .0838384{col 61}{space 1}   -3.32{col 70}{space 3}0.001{col 78}{space 4} .5076354{col 91}{space 3} .8398595
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.106137{col 50}{space 2} .1468444{col 61}{space 1}    0.76{col 70}{space 3}0.447{col 78}{space 4}  .852656{col 91}{space 3} 1.434974
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .5892368{col 50}{space 2} .0900676{col 61}{space 1}   -3.46{col 70}{space 3}0.001{col 78}{space 4} .4366566{col 91}{space 3} .7951329
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}  .849199{col 50}{space 2} .2281017{col 61}{space 1}   -0.61{col 70}{space 3}0.543{col 78}{space 4} .5015336{col 91}{space 3} 1.437868
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.072952{col 50}{space 2} .2137632{col 61}{space 1}    0.35{col 70}{space 3}0.724{col 78}{space 4} .7260127{col 91}{space 3} 1.585682
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5238108{col 50}{space 2} .2125478{col 61}{space 1}   -1.59{col 70}{space 3}0.111{col 78}{space 4}  .236415{col 91}{space 3} 1.160577
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .8804051{col 50}{space 2} .1915187{col 61}{space 1}   -0.59{col 70}{space 3}0.558{col 78}{space 4} .5747249{col 91}{space 3} 1.348668
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .6408041{col 50}{space 2} .1391445{col 61}{space 1}   -2.05{col 70}{space 3}0.040{col 78}{space 4} .4186379{col 91}{space 3} .9808713
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .7738245{col 50}{space 2} .1608112{col 61}{space 1}   -1.23{col 70}{space 3}0.217{col 78}{space 4} .5148696{col 91}{space 3} 1.163022
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .5645058{col 50}{space 2} .1274512{col 61}{space 1}   -2.53{col 70}{space 3}0.011{col 78}{space 4} .3626017{col 91}{space 3} .8788345
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.240085{col 50}{space 2} .2334789{col 61}{space 1}    1.14{col 70}{space 3}0.253{col 78}{space 4} .8573168{col 91}{space 3} 1.793749
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.238847{col 50}{space 2} .3908825{col 61}{space 1}    0.68{col 70}{space 3}0.497{col 78}{space 4} .6673647{col 91}{space 3} 2.299703
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.287317{col 50}{space 2} .2907146{col 61}{space 1}    1.12{col 70}{space 3}0.263{col 78}{space 4} .8267987{col 91}{space 3} 2.004338
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 2.010093{col 50}{space 2} .5449394{col 61}{space 1}    2.58{col 70}{space 3}0.010{col 78}{space 4} 1.181364{col 91}{space 3} 3.420178
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .7500211{col 50}{space 2} .1116783{col 61}{space 1}   -1.93{col 70}{space 3}0.053{col 78}{space 4} .5601323{col 91}{space 3} 1.004284
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 2.376303{col 50}{space 2} .7231256{col 61}{space 1}    2.84{col 70}{space 3}0.004{col 78}{space 4} 1.308567{col 91}{space 3} 4.315265
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 2.171363{col 50}{space 2} .6616116{col 61}{space 1}    2.54{col 70}{space 3}0.011{col 78}{space 4} 1.194794{col 91}{space 3} 3.946133
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .8999696{col 50}{space 2} .3310424{col 61}{space 1}   -0.29{col 70}{space 3}0.774{col 78}{space 4} .4375508{col 91}{space 3} 1.851088
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.086634{col 50}{space 2} .2196671{col 61}{space 1}    0.41{col 70}{space 3}0.681{col 78}{space 4}  .731067{col 91}{space 3} 1.615137
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} .9521414{col 50}{space 2} .1698551{col 61}{space 1}   -0.27{col 70}{space 3}0.783{col 78}{space 4} .6711304{col 91}{space 3} 1.350815
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .0849039{col 50}{space 2} .0343544{col 61}{space 1}   -6.10{col 70}{space 3}0.000{col 78}{space 4} .0384063{col 91}{space 3} .1876947
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 2: Improve 
. svy: logit import_improve ib2.employmentstatus5 if infullmodel_TableD3 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   1{txt},{res}   3962{txt}){col 67}= {res}      5.33
{txt}{col 49}Prob > F{col 67}= {res}    0.0210

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}  Linearized
{col 1}   import_improve{col 19}{c |} Odds Ratio{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
employmentstatus5 {c |}
{space 7}FT Worker  {c |}{col 19}{res}{space 2} 1.718989{col 31}{space 2} .4033913{col 42}{space 1}    2.31{col 51}{space 3}0.021{col 59}{space 4} 1.085082{col 72}{space 3} 2.723226
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} .0289373{col 31}{space 2} .0061608{col 42}{space 1}  -16.64{col 51}{space 3}0.000{col 59}{space 4} .0190625{col 72}{space 3} .0439273
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit import_improve ib2.employmentstatus5 $controls_demographics if infullmodel_TableD3 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      3.53
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                      import_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} 1.876347{col 50}{space 2} .5102066{col 61}{space 1}    2.31{col 70}{space 3}0.021{col 78}{space 4} 1.101002{col 91}{space 3} 3.197703
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .4813711{col 50}{space 2} .0989775{col 61}{space 1}   -3.56{col 70}{space 3}0.000{col 78}{space 4} .3216666{col 91}{space 3} .7203673
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .2059369{col 50}{space 2} .0579086{col 61}{space 1}   -5.62{col 70}{space 3}0.000{col 78}{space 4} .1186607{col 91}{space 3} .3574057
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}  .819794{col 50}{space 2}  .148332{col 61}{space 1}   -1.10{col 70}{space 3}0.272{col 78}{space 4} .5749669{col 91}{space 3} 1.168871
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.022786{col 50}{space 2} .2006774{col 61}{space 1}    0.11{col 70}{space 3}0.909{col 78}{space 4} .6961809{col 91}{space 3} 1.502613
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .7019361{col 50}{space 2} .1437957{col 61}{space 1}   -1.73{col 70}{space 3}0.084{col 78}{space 4} .4697542{col 91}{space 3} 1.048877
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9356342{col 50}{space 2} .3569388{col 61}{space 1}   -0.17{col 70}{space 3}0.862{col 78}{space 4} .4428725{col 91}{space 3} 1.976667
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.143594{col 50}{space 2} .4221313{col 61}{space 1}    0.36{col 70}{space 3}0.716{col 78}{space 4} .5545935{col 91}{space 3} 2.358138
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.157162{col 50}{space 2} .5955343{col 61}{space 1}    0.28{col 70}{space 3}0.777{col 78}{space 4} .4218802{col 91}{space 3} 3.173946
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .3727353{col 50}{space 2}  .115085{col 61}{space 1}   -3.20{col 70}{space 3}0.001{col 78}{space 4} .2034721{col 91}{space 3}  .682804
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .3385937{col 50}{space 2}  .109652{col 61}{space 1}   -3.34{col 70}{space 3}0.001{col 78}{space 4} .1794474{col 91}{space 3}  .638882
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .4079394{col 50}{space 2} .1267116{col 61}{space 1}   -2.89{col 70}{space 3}0.004{col 78}{space 4}  .221881{col 91}{space 3} .7500174
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .2649332{col 50}{space 2} .0865925{col 61}{space 1}   -4.06{col 70}{space 3}0.000{col 78}{space 4} .1395851{col 91}{space 3} .5028447
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.498316{col 50}{space 2} .3613864{col 61}{space 1}    1.68{col 70}{space 3}0.094{col 78}{space 4} .9337597{col 91}{space 3} 2.404207
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}  .718643{col 50}{space 2} .2206643{col 61}{space 1}   -1.08{col 70}{space 3}0.282{col 78}{space 4} .3936099{col 91}{space 3}  1.31208
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .7920011{col 50}{space 2}  .249908{col 61}{space 1}   -0.74{col 70}{space 3}0.460{col 78}{space 4} .4266342{col 91}{space 3} 1.470266
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .9644461{col 50}{space 2} .3338255{col 61}{space 1}   -0.10{col 70}{space 3}0.917{col 78}{space 4} .4892828{col 91}{space 3} 1.901061
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .1727317{col 50}{space 2} .0736412{col 61}{space 1}   -4.12{col 70}{space 3}0.000{col 78}{space 4}   .07488{col 91}{space 3} .3984539
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit import_improve ib2.employmentstatus5 $controls_demographics $controls_objective if infullmodel_TableD3 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      2.81
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                      import_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} 1.879579{col 50}{space 2} .5135289{col 61}{space 1}    2.31{col 70}{space 3}0.021{col 78}{space 4} 1.100091{col 91}{space 3} 3.211387
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .4859691{col 50}{space 2}  .100689{col 61}{space 1}   -3.48{col 70}{space 3}0.001{col 78}{space 4} .3237371{col 91}{space 3} .7294992
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .2047739{col 50}{space 2} .0580106{col 61}{space 1}   -5.60{col 70}{space 3}0.000{col 78}{space 4} .1175069{col 91}{space 3} .3568502
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .8222797{col 50}{space 2} .1492892{col 61}{space 1}   -1.08{col 70}{space 3}0.281{col 78}{space 4} .5760129{col 91}{space 3} 1.173835
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.031034{col 50}{space 2} .2059519{col 61}{space 1}    0.15{col 70}{space 3}0.878{col 78}{space 4} .6969337{col 91}{space 3} 1.525299
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .6794348{col 50}{space 2} .1425353{col 61}{space 1}   -1.84{col 70}{space 3}0.066{col 78}{space 4} .4503227{col 91}{space 3} 1.025113
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .9105414{col 50}{space 2} .3478142{col 61}{space 1}   -0.25{col 70}{space 3}0.806{col 78}{space 4} .4305793{col 91}{space 3} 1.925512
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.146478{col 50}{space 2} .4223236{col 61}{space 1}    0.37{col 70}{space 3}0.711{col 78}{space 4} .5568215{col 91}{space 3} 2.360561
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 1.159751{col 50}{space 2} .5901265{col 61}{space 1}    0.29{col 70}{space 3}0.771{col 78}{space 4} .4276692{col 91}{space 3} 3.145007
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .3777598{col 50}{space 2} .1154978{col 61}{space 1}   -3.18{col 70}{space 3}0.001{col 78}{space 4} .2074371{col 91}{space 3} .6879312
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .3441002{col 50}{space 2} .1114877{col 61}{space 1}   -3.29{col 70}{space 3}0.001{col 78}{space 4} .1823111{col 91}{space 3} .6494664
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .4077277{col 50}{space 2} .1272669{col 61}{space 1}   -2.87{col 70}{space 3}0.004{col 78}{space 4} .2211045{col 91}{space 3} .7518703
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .2705826{col 50}{space 2} .0887859{col 61}{space 1}   -3.98{col 70}{space 3}0.000{col 78}{space 4} .1422037{col 91}{space 3} .5148598
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.453146{col 50}{space 2} .4086095{col 61}{space 1}    1.33{col 70}{space 3}0.184{col 78}{space 4} .8373113{col 91}{space 3}  2.52192
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.159795{col 50}{space 2}  .515801{col 61}{space 1}    0.33{col 70}{space 3}0.739{col 78}{space 4} .4849612{col 91}{space 3} 2.773676
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .7782851{col 50}{space 2} .3033448{col 61}{space 1}   -0.64{col 70}{space 3}0.520{col 78}{space 4} .3624704{col 91}{space 3} 1.671109
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .9747707{col 50}{space 2} .5281169{col 61}{space 1}   -0.05{col 70}{space 3}0.962{col 78}{space 4} .3369711{col 91}{space 3} 2.819761
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .9232136{col 50}{space 2} .2310483{col 61}{space 1}   -0.32{col 70}{space 3}0.750{col 78}{space 4} .5652115{col 91}{space 3} 1.507973
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} .9426035{col 50}{space 2} .4463135{col 61}{space 1}   -0.12{col 70}{space 3}0.901{col 78}{space 4} .3725377{col 91}{space 3} 2.384997
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.144866{col 50}{space 2} .5556917{col 61}{space 1}    0.28{col 70}{space 3}0.780{col 78}{space 4} .4420531{col 91}{space 3} 2.965068
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .5611062{col 50}{space 2} .3015494{col 61}{space 1}   -1.08{col 70}{space 3}0.282{col 78}{space 4} .1956379{col 91}{space 3} 1.609301
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.090376{col 50}{space 2} .3555053{col 61}{space 1}    0.27{col 70}{space 3}0.791{col 78}{space 4} .5753957{col 91}{space 3} 2.066266
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2}  1.36281{col 50}{space 2} .3875331{col 61}{space 1}    1.09{col 70}{space 3}0.276{col 78}{space 4} .7803887{col 91}{space 3} 2.379906
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .1538431{col 50}{space 2} .0838747{col 61}{space 1}   -3.43{col 70}{space 3}0.001{col 78}{space 4}  .052828{col 91}{space 3} .4480145
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 3: Worsen v Improve
. svy: logit import_directionworse ib2.employmentstatus5 if infullmodel_TableD3 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       533
{txt}{col 1}Number of PSUs{col 20}= {res}      533{txt}{col 49}Population size{col 67}={res} 592.486062
{txt}{col 49}Design df{col 67}= {res}       532
{txt}{col 49}F({res}   1{txt},{res}    532{txt}){col 67}= {res}      3.26
{txt}{col 49}Prob > F{col 67}= {res}    0.0716

{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}  Linearized
{col 1}import_directionworse{col 23}{c |} Odds Ratio{col 35}   Std. Err.{col 47}      t{col 55}   P>|t|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}employmentstatus5 {c |}
{space 11}FT Worker  {c |}{col 23}{res}{space 2} .6172289{col 35}{space 2}  .165001{col 46}{space 1}   -1.80{col 55}{space 3}0.072{col 63}{space 4} .3650728{col 76}{space 3} 1.043549
{txt}{space 16}_cons {c |}{col 23}{res}{space 2} 3.531656{col 35}{space 2} .8507109{col 46}{space 1}    5.24{col 55}{space 3}0.000{col 63}{space 4} 2.200251{col 76}{space 3} 5.668716
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit import_directionworse ib2.employmentstatus5 $controls_demographics if infullmodel_TableD3 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       533
{txt}{col 1}Number of PSUs{col 20}= {res}      533{txt}{col 49}Population size{col 67}={res} 592.486062
{txt}{col 49}Design df{col 67}= {res}       532
{txt}{col 49}F({res}  17{txt},{res}    516{txt}){col 67}= {res}      2.28
{txt}{col 49}Prob > F{col 67}= {res}    0.0026

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}               import_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} .5755386{col 50}{space 2} .1691663{col 61}{space 1}   -1.88{col 70}{space 3}0.061{col 78}{space 4} .3230821{col 91}{space 3} 1.025265
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 2.142296{col 50}{space 2} .5710227{col 61}{space 1}    2.86{col 70}{space 3}0.004{col 78}{space 4} 1.269044{col 91}{space 3} 3.616447
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 4.581218{col 50}{space 2} 1.514217{col 61}{space 1}    4.60{col 70}{space 3}0.000{col 78}{space 4} 2.393284{col 91}{space 3} 8.769355
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .7548422{col 50}{space 2} .1706943{col 61}{space 1}   -1.24{col 70}{space 3}0.214{col 78}{space 4} .4840977{col 91}{space 3} 1.177008
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.101412{col 50}{space 2} .2589913{col 61}{space 1}    0.41{col 70}{space 3}0.681{col 78}{space 4} .6939658{col 91}{space 3} 1.748082
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9391509{col 50}{space 2} .2415127{col 61}{space 1}   -0.24{col 70}{space 3}0.807{col 78}{space 4} .5666837{col 91}{space 3} 1.556432
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .8300581{col 50}{space 2} .3940198{col 61}{space 1}   -0.39{col 70}{space 3}0.695{col 78}{space 4} .3266862{col 91}{space 3} 2.109047
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 1.013492{col 50}{space 2} .4191468{col 61}{space 1}    0.03{col 70}{space 3}0.974{col 78}{space 4} .4497681{col 91}{space 3} 2.283767
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2}  .549621{col 50}{space 2} .3468396{col 61}{space 1}   -0.95{col 70}{space 3}0.343{col 78}{space 4} .1591057{col 91}{space 3} 1.898632
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.887767{col 50}{space 2} .6886964{col 61}{space 1}    1.74{col 70}{space 3}0.082{col 78}{space 4} .9219434{col 91}{space 3} 3.865381
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.645772{col 50}{space 2} .6414268{col 61}{space 1}    1.28{col 70}{space 3}0.202{col 78}{space 4}  .765357{col 91}{space 3} 3.538957
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.596431{col 50}{space 2} .5581033{col 61}{space 1}    1.34{col 70}{space 3}0.181{col 78}{space 4} .8033346{col 91}{space 3} 3.172517
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.797841{col 50}{space 2} .6174862{col 61}{space 1}    1.71{col 70}{space 3}0.088{col 78}{space 4}  .915653{col 91}{space 3} 3.529973
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .9201556{col 50}{space 2} .2876195{col 61}{space 1}   -0.27{col 70}{space 3}0.790{col 78}{space 4} .4979533{col 91}{space 3} 1.700333
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.676232{col 50}{space 2} .6010057{col 61}{space 1}    1.44{col 70}{space 3}0.150{col 78}{space 4} .8287885{col 91}{space 3} 3.390195
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.546733{col 50}{space 2} .5325191{col 61}{space 1}    1.27{col 70}{space 3}0.206{col 78}{space 4}  .786484{col 91}{space 3}  3.04187
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.556283{col 50}{space 2} .5677038{col 61}{space 1}    1.21{col 70}{space 3}0.226{col 78}{space 4} .7601124{col 91}{space 3} 3.186394
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 1.006613{col 50}{space 2} .4765715{col 61}{space 1}    0.01{col 70}{space 3}0.989{col 78}{space 4} .3971462{col 91}{space 3} 2.551378
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit import_directionworse ib2.employmentstatus5 $controls_demographics $controls_objective if infullmodel_TableD3 == 1, or //*M2 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       533
{txt}{col 1}Number of PSUs{col 20}= {res}      533{txt}{col 49}Population size{col 67}={res} 592.486062
{txt}{col 49}Design df{col 67}= {res}       532
{txt}{col 49}F({res}  23{txt},{res}    510{txt}){col 67}= {res}      1.76
{txt}{col 49}Prob > F{col 67}= {res}    0.0165

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}               import_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} .5731932{col 50}{space 2}   .17438{col 61}{space 1}   -1.83{col 70}{space 3}0.068{col 78}{space 4} .3153213{col 91}{space 3} 1.041955
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 2.313153{col 50}{space 2} .6313656{col 61}{space 1}    3.07{col 70}{space 3}0.002{col 78}{space 4}  1.35314{col 91}{space 3} 3.954268
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 4.734086{col 50}{space 2} 1.610438{col 61}{space 1}    4.57{col 70}{space 3}0.000{col 78}{space 4} 2.426693{col 91}{space 3} 9.235438
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .7399686{col 50}{space 2} .1671828{col 61}{space 1}   -1.33{col 70}{space 3}0.183{col 78}{space 4} .4747455{col 91}{space 3} 1.153362
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.080401{col 50}{space 2} .2550306{col 61}{space 1}    0.33{col 70}{space 3}0.743{col 78}{space 4} .6795148{col 91}{space 3} 1.717793
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .9074859{col 50}{space 2} .2415928{col 61}{space 1}   -0.36{col 70}{space 3}0.716{col 78}{space 4}  .537916{col 91}{space 3} 1.530965
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .7555217{col 50}{space 2} .3661597{col 61}{space 1}   -0.58{col 70}{space 3}0.563{col 78}{space 4} .2915922{col 91}{space 3} 1.957573
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2}   1.0286{col 50}{space 2} .4404442{col 61}{space 1}    0.07{col 70}{space 3}0.948{col 78}{space 4} .4435402{col 91}{space 3} 2.385395
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5997226{col 50}{space 2} .3572605{col 61}{space 1}   -0.86{col 70}{space 3}0.391{col 78}{space 4}  .186091{col 91}{space 3} 1.932749
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.779749{col 50}{space 2} .6540373{col 61}{space 1}    1.57{col 70}{space 3}0.117{col 78}{space 4} .8646471{col 91}{space 3} 3.663354
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.501792{col 50}{space 2} .5975836{col 61}{space 1}    1.02{col 70}{space 3}0.307{col 78}{space 4} .6872787{col 91}{space 3} 3.281609
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.504137{col 50}{space 2} .5283807{col 61}{space 1}    1.16{col 70}{space 3}0.246{col 78}{space 4} .7543821{col 91}{space 3} 2.999049
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.719397{col 50}{space 2} .5994877{col 61}{space 1}    1.55{col 70}{space 3}0.121{col 78}{space 4} .8667983{col 91}{space 3} 3.410626
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.015425{col 50}{space 2} .3558767{col 61}{space 1}    0.04{col 70}{space 3}0.965{col 78}{space 4} .5100897{col 91}{space 3} 2.021386
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.325183{col 50}{space 2} .6481626{col 61}{space 1}    0.58{col 70}{space 3}0.565{col 78}{space 4} .5069834{col 91}{space 3} 3.463841
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.595465{col 50}{space 2} .7151305{col 61}{space 1}    1.04{col 70}{space 3}0.298{col 78}{space 4} .6614326{col 91}{space 3} 3.848476
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.431199{col 50}{space 2} .8129854{col 61}{space 1}    0.63{col 70}{space 3}0.528{col 78}{space 4} .4688963{col 91}{space 3} 4.368409
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .8610504{col 50}{space 2}  .276007{col 61}{space 1}   -0.47{col 70}{space 3}0.641{col 78}{space 4} .4587292{col 91}{space 3} 1.616221
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.740808{col 50}{space 2} .9973024{col 61}{space 1}    0.97{col 70}{space 3}0.334{col 78}{space 4} .5649221{col 91}{space 3}   5.3643
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.508786{col 50}{space 2} .8598458{col 61}{space 1}    0.72{col 70}{space 3}0.471{col 78}{space 4} .4925246{col 91}{space 3}  4.62197
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} 1.317195{col 50}{space 2} .7961797{col 61}{space 1}    0.46{col 70}{space 3}0.649{col 78}{space 4} .4017605{col 91}{space 3} 4.318499
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .9289508{col 50}{space 2} .3635688{col 61}{space 1}   -0.19{col 70}{space 3}0.851{col 78}{space 4} .4306193{col 91}{space 3} 2.003973
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} .6042082{col 50}{space 2} .2071826{col 61}{space 1}   -1.47{col 70}{space 3}0.142{col 78}{space 4} .3080666{col 91}{space 3} 1.185028
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .9221997{col 50}{space 2} .5596258{col 61}{space 1}   -0.13{col 70}{space 3}0.894{col 78}{space 4} .2799663{col 91}{space 3} 3.037696
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}.         
.         
.         
.         
. ************* TABLE D4: Demographic Predictors of Subjective Exposure to Cheap Imports ***************
. 
. svy: logit immigration_worsen aiexposure_felten2021  $controls_demographics $controls_objective, or
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}note: aiexposure_felten2021 omitted because of collinearity

Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      6.22
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                  immigration_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .7326398{col 50}{space 2}   .08772{col 61}{space 1}   -2.60{col 70}{space 3}0.009{col 78}{space 4} .5793538{col 91}{space 3} .9264825
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.494482{col 50}{space 2} .2352497{col 61}{space 1}    2.55{col 70}{space 3}0.011{col 78}{space 4} 1.097641{col 91}{space 3} 2.034797
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.610733{col 50}{space 2} .2628841{col 61}{space 1}    2.92{col 70}{space 3}0.004{col 78}{space 4} 1.169657{col 91}{space 3} 2.218139
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}  .599543{col 50}{space 2} .0587938{col 61}{space 1}   -5.22{col 70}{space 3}0.000{col 78}{space 4} .4946776{col 91}{space 3} .7266385
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7254971{col 50}{space 2} .0774474{col 61}{space 1}   -3.01{col 70}{space 3}0.003{col 78}{space 4} .5884933{col 91}{space 3} .8943961
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.018511{col 50}{space 2} .1252356{col 61}{space 1}    0.15{col 70}{space 3}0.881{col 78}{space 4} .8003321{col 91}{space 3} 1.296167
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2}  .822743{col 50}{space 2} .0896053{col 61}{space 1}   -1.79{col 70}{space 3}0.073{col 78}{space 4} .6645552{col 91}{space 3} 1.018585
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .6033868{col 50}{space 2} .1581631{col 61}{space 1}   -1.93{col 70}{space 3}0.054{col 78}{space 4} .3609159{col 91}{space 3} 1.008755
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .7067927{col 50}{space 2} .1216358{col 61}{space 1}   -2.02{col 70}{space 3}0.044{col 78}{space 4} .5043818{col 91}{space 3} .9904322
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5794473{col 50}{space 2} .1826008{col 61}{space 1}   -1.73{col 70}{space 3}0.083{col 78}{space 4} .3123873{col 91}{space 3} 1.074817
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .6693483{col 50}{space 2} .1154689{col 61}{space 1}   -2.33{col 70}{space 3}0.020{col 78}{space 4} .4772732{col 91}{space 3} .9387227
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .7129928{col 50}{space 2} .1187527{col 61}{space 1}   -2.03{col 70}{space 3}0.042{col 78}{space 4} .5143631{col 91}{space 3} .9883268
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .7970378{col 50}{space 2} .1319035{col 61}{space 1}   -1.37{col 70}{space 3}0.171{col 78}{space 4}  .576194{col 91}{space 3} 1.102527
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .5898411{col 50}{space 2} .1036875{col 61}{space 1}   -3.00{col 70}{space 3}0.003{col 78}{space 4} .4178858{col 91}{space 3} .8325541
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .8419726{col 50}{space 2} .1299202{col 61}{space 1}   -1.11{col 70}{space 3}0.265{col 78}{space 4} .6221765{col 91}{space 3} 1.139416
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.219233{col 50}{space 2} .2865309{col 61}{space 1}    0.84{col 70}{space 3}0.399{col 78}{space 4}  .769105{col 91}{space 3} 1.932805
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .8823133{col 50}{space 2} .1578064{col 61}{space 1}   -0.70{col 70}{space 3}0.484{col 78}{space 4} .6213473{col 91}{space 3} 1.252885
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.365186{col 50}{space 2} .2966848{col 61}{space 1}    1.43{col 70}{space 3}0.152{col 78}{space 4} .8915596{col 91}{space 3} 2.090417
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2}        1{col 50}{txt}  (omitted)
{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.905536{col 50}{space 2} .4550764{col 61}{space 1}    2.70{col 70}{space 3}0.007{col 78}{space 4} 1.193089{col 91}{space 3} 3.043418
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2}  1.46295{col 50}{space 2} .3511115{col 61}{space 1}    1.59{col 70}{space 3}0.113{col 78}{space 4} .9138537{col 91}{space 3} 2.341975
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .8878147{col 50}{space 2} .2416446{col 61}{space 1}   -0.44{col 70}{space 3}0.662{col 78}{space 4} .5206817{col 91}{space 3} 1.513814
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.083534{col 50}{space 2} .1647442{col 61}{space 1}    0.53{col 70}{space 3}0.598{col 78}{space 4} .8042362{col 91}{space 3} 1.459828
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} .7759497{col 50}{space 2} .1117268{col 61}{space 1}   -1.76{col 70}{space 3}0.078{col 78}{space 4} .5851052{col 91}{space 3} 1.029042
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2}  .295907{col 50}{space 2} .0809559{col 61}{space 1}   -4.45{col 70}{space 3}0.000{col 78}{space 4} .1730643{col 91}{space 3} .5059447
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. generate infullmodel_TableD4 = e(sample) 
{txt}
{com}.          
.          
. ******* Age Group ********
. 
. * Model 1: Worsen 
. svy: logit immigration_worsen ib3.age_3 if infullmodel_TableD4 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   2{txt},{res}   3961{txt}){col 67}= {res}      8.31
{txt}{col 49}Prob > F{col 67}= {res}    0.0003

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}  Linearized
{col 1}immigration_worsen{col 20}{c |} Odds Ratio{col 32}   Std. Err.{col 44}      t{col 52}   P>|t|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}age_3group {c |}
{space 12}18-29  {c |}{col 20}{res}{space 2} .5359472{col 32}{space 2} .0838943{col 43}{space 1}   -3.98{col 52}{space 3}0.000{col 60}{space 4} .3943108{col 73}{space 3} .7284593
{txt}{space 12}30-49  {c |}{col 20}{res}{space 2} .8153386{col 32}{space 2}  .078676{col 43}{space 1}   -2.12{col 52}{space 3}0.034{col 60}{space 4}  .674802{col 73}{space 3} .9851439
{txt}{space 18} {c |}
{space 13}_cons {c |}{col 20}{res}{space 2} .2898769{col 32}{space 2} .0203554{col 43}{space 1}  -17.63{col 52}{space 3}0.000{col 60}{space 4} .2525942{col 73}{space 3} .3326625
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit immigration_worsen ib3.age_3 $controls_demographics if infullmodel_TableD4 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      7.86
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                  immigration_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}age_3group {c |}
{space 30}18-29  {c |}{col 38}{res}{space 2} .6210965{col 50}{space 2} .1013545{col 61}{space 1}   -2.92{col 70}{space 3}0.004{col 78}{space 4}  .451037{col 91}{space 3} .8552754
{txt}{space 30}30-49  {c |}{col 38}{res}{space 2} .9271055{col 50}{space 2} .0946921{col 61}{space 1}   -0.74{col 70}{space 3}0.459{col 78}{space 4} .7588626{col 91}{space 3} 1.132649
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .5958004{col 50}{space 2} .0580761{col 61}{space 1}   -5.31{col 70}{space 3}0.000{col 78}{space 4} .4921573{col 91}{space 3} .7212696
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7248211{col 50}{space 2}  .076892{col 61}{space 1}   -3.03{col 70}{space 3}0.002{col 78}{space 4} .5887139{col 91}{space 3} .8923955
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.042172{col 50}{space 2} .1260904{col 61}{space 1}    0.34{col 70}{space 3}0.733{col 78}{space 4} .8220966{col 91}{space 3} 1.321163
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .8324747{col 50}{space 2} .0880657{col 61}{space 1}   -1.73{col 70}{space 3}0.083{col 78}{space 4} .6765451{col 91}{space 3} 1.024343
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .5974396{col 50}{space 2} .1563604{col 61}{space 1}   -1.97{col 70}{space 3}0.049{col 78}{space 4} .3576446{col 91}{space 3} .9980136
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .7180797{col 50}{space 2}  .123263{col 61}{space 1}   -1.93{col 70}{space 3}0.054{col 78}{space 4} .5128777{col 91}{space 3} 1.005383
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5878524{col 50}{space 2} .1865065{col 61}{space 1}   -1.67{col 70}{space 3}0.094{col 78}{space 4} .3155928{col 91}{space 3} 1.094988
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .6709222{col 50}{space 2} .1149382{col 61}{space 1}   -2.33{col 70}{space 3}0.020{col 78}{space 4} .4795182{col 91}{space 3} .9387268
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .7103922{col 50}{space 2} .1180463{col 61}{space 1}   -2.06{col 70}{space 3}0.040{col 78}{space 4} .5128735{col 91}{space 3} .9839796
{txt}{space 34}4  {c |}{col 38}{res}{space 2}  .780437{col 50}{space 2} .1288428{col 61}{space 1}   -1.50{col 70}{space 3}0.133{col 78}{space 4} .5646373{col 91}{space 3} 1.078713
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .5720486{col 50}{space 2}  .100061{col 61}{space 1}   -3.19{col 70}{space 3}0.001{col 78}{space 4} .4059736{col 91}{space 3} .8060612
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .8561006{col 50}{space 2} .1220303{col 61}{space 1}   -1.09{col 70}{space 3}0.276{col 78}{space 4} .6473746{col 91}{space 3} 1.132124
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.278422{col 50}{space 2} .1836731{col 61}{space 1}    1.71{col 70}{space 3}0.087{col 78}{space 4} .9645912{col 91}{space 3} 1.694358
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.037038{col 50}{space 2} .1644867{col 61}{space 1}    0.23{col 70}{space 3}0.819{col 78}{space 4} .7598757{col 91}{space 3} 1.415294
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}   1.7023{col 50}{space 2} .2648454{col 61}{space 1}    3.42{col 70}{space 3}0.001{col 78}{space 4} 1.254772{col 91}{space 3} 2.309443
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .5402568{col 50}{space 2}   .10221{col 61}{space 1}   -3.25{col 70}{space 3}0.001{col 78}{space 4} .3728328{col 91}{space 3}  .782864
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit immigration_worsen ib3.age_3 $controls_demographics  $controls_objective if infullmodel_TableD4 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      6.22
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                  immigration_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}age_3group {c |}
{space 30}18-29  {c |}{col 38}{res}{space 2} .6208352{col 50}{space 2} .1013251{col 61}{space 1}   -2.92{col 70}{space 3}0.004{col 78}{space 4} .4508283{col 91}{space 3} .8549514
{txt}{space 30}30-49  {c |}{col 38}{res}{space 2} .9278271{col 50}{space 2} .0952678{col 61}{space 1}   -0.73{col 70}{space 3}0.466{col 78}{space 4} .7586479{col 91}{space 3} 1.134733
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}  .599543{col 50}{space 2} .0587938{col 61}{space 1}   -5.22{col 70}{space 3}0.000{col 78}{space 4} .4946776{col 91}{space 3} .7266385
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7254971{col 50}{space 2} .0774474{col 61}{space 1}   -3.01{col 70}{space 3}0.003{col 78}{space 4} .5884933{col 91}{space 3} .8943961
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.018511{col 50}{space 2} .1252356{col 61}{space 1}    0.15{col 70}{space 3}0.881{col 78}{space 4} .8003321{col 91}{space 3} 1.296167
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2}  .822743{col 50}{space 2} .0896053{col 61}{space 1}   -1.79{col 70}{space 3}0.073{col 78}{space 4} .6645552{col 91}{space 3} 1.018585
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .6033868{col 50}{space 2} .1581631{col 61}{space 1}   -1.93{col 70}{space 3}0.054{col 78}{space 4} .3609159{col 91}{space 3} 1.008755
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .7067927{col 50}{space 2} .1216358{col 61}{space 1}   -2.02{col 70}{space 3}0.044{col 78}{space 4} .5043818{col 91}{space 3} .9904322
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5794473{col 50}{space 2} .1826008{col 61}{space 1}   -1.73{col 70}{space 3}0.083{col 78}{space 4} .3123873{col 91}{space 3} 1.074817
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .6693483{col 50}{space 2} .1154689{col 61}{space 1}   -2.33{col 70}{space 3}0.020{col 78}{space 4} .4772732{col 91}{space 3} .9387227
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .7129928{col 50}{space 2} .1187527{col 61}{space 1}   -2.03{col 70}{space 3}0.042{col 78}{space 4} .5143631{col 91}{space 3} .9883268
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .7970378{col 50}{space 2} .1319035{col 61}{space 1}   -1.37{col 70}{space 3}0.171{col 78}{space 4}  .576194{col 91}{space 3} 1.102527
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .5898411{col 50}{space 2} .1036875{col 61}{space 1}   -3.00{col 70}{space 3}0.003{col 78}{space 4} .4178858{col 91}{space 3} .8325541
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .8419726{col 50}{space 2} .1299202{col 61}{space 1}   -1.11{col 70}{space 3}0.265{col 78}{space 4} .6221765{col 91}{space 3} 1.139416
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.219233{col 50}{space 2} .2865309{col 61}{space 1}    0.84{col 70}{space 3}0.399{col 78}{space 4}  .769105{col 91}{space 3} 1.932805
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .8823133{col 50}{space 2} .1578064{col 61}{space 1}   -0.70{col 70}{space 3}0.484{col 78}{space 4} .6213473{col 91}{space 3} 1.252885
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.365186{col 50}{space 2} .2966848{col 61}{space 1}    1.43{col 70}{space 3}0.152{col 78}{space 4} .8915596{col 91}{space 3} 2.090417
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .7326398{col 50}{space 2}   .08772{col 61}{space 1}   -2.60{col 70}{space 3}0.009{col 78}{space 4} .5793538{col 91}{space 3} .9264825
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.905536{col 50}{space 2} .4550764{col 61}{space 1}    2.70{col 70}{space 3}0.007{col 78}{space 4} 1.193089{col 91}{space 3} 3.043418
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2}  1.46295{col 50}{space 2} .3511115{col 61}{space 1}    1.59{col 70}{space 3}0.113{col 78}{space 4} .9138537{col 91}{space 3} 2.341975
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .8878147{col 50}{space 2} .2416446{col 61}{space 1}   -0.44{col 70}{space 3}0.662{col 78}{space 4} .5206817{col 91}{space 3} 1.513814
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.083534{col 50}{space 2} .1647442{col 61}{space 1}    0.53{col 70}{space 3}0.598{col 78}{space 4} .8042362{col 91}{space 3} 1.459828
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} .7759497{col 50}{space 2} .1117268{col 61}{space 1}   -1.76{col 70}{space 3}0.078{col 78}{space 4} .5851052{col 91}{space 3} 1.029042
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .4766273{col 50}{space 2} .1144469{col 61}{space 1}   -3.09{col 70}{space 3}0.002{col 78}{space 4} .2976648{col 91}{space 3} .7631859
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 2: Improve 
. svy: logit immigration_improve ib3.age_3 if infullmodel_TableD4 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   2{txt},{res}   3961{txt}){col 67}= {res}     21.02
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}  Linearized
{col 1}immigration_improve{col 21}{c |} Odds Ratio{col 33}   Std. Err.{col 45}      t{col 53}   P>|t|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}age_3group {c |}
{space 13}18-29  {c |}{col 21}{res}{space 2} 4.920409{col 33}{space 2} 1.214364{col 44}{space 1}    6.46{col 53}{space 3}0.000{col 61}{space 4} 3.032908{col 74}{space 3} 7.982576
{txt}{space 13}30-49  {c |}{col 21}{res}{space 2} 2.640243{col 33}{space 2}  .555509{col 44}{space 1}    4.61{col 53}{space 3}0.000{col 61}{space 4} 1.747816{col 74}{space 3} 3.988339
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2} .0230311{col 33}{space 2} .0042049{col 44}{space 1}  -20.65{col 53}{space 3}0.000{col 61}{space 4} .0161012{col 74}{space 3} .0329435
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit immigration_improve ib3.age_3 $controls_demographics if infullmodel_TableD4 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      8.18
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                 immigration_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}age_3group {c |}
{space 30}18-29  {c |}{col 38}{res}{space 2} 4.769197{col 50}{space 2} 1.270343{col 61}{space 1}    5.86{col 70}{space 3}0.000{col 78}{space 4} 2.829087{col 91}{space 3}  8.03978
{txt}{space 30}30-49  {c |}{col 38}{res}{space 2} 2.498517{col 50}{space 2} .5729564{col 61}{space 1}    3.99{col 70}{space 3}0.000{col 78}{space 4} 1.593773{col 91}{space 3} 3.916863
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6261418{col 50}{space 2} .1042278{col 61}{space 1}   -2.81{col 70}{space 3}0.005{col 78}{space 4} .4517915{col 91}{space 3} .8677754
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.582045{col 50}{space 2} .2810231{col 61}{space 1}    2.58{col 70}{space 3}0.010{col 78}{space 4} 1.116789{col 91}{space 3} 2.241126
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .8706222{col 50}{space 2} .2078136{col 61}{space 1}   -0.58{col 70}{space 3}0.562{col 78}{space 4} .5452426{col 91}{space 3} 1.390176
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.493342{col 50}{space 2} .2813514{col 61}{space 1}    2.13{col 70}{space 3}0.033{col 78}{space 4} 1.032144{col 91}{space 3} 2.160617
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.580833{col 50}{space 2} .5289845{col 61}{space 1}    1.37{col 70}{space 3}0.171{col 78}{space 4} .8202885{col 91}{space 3}  3.04653
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 2.312422{col 50}{space 2} .6619176{col 61}{space 1}    2.93{col 70}{space 3}0.003{col 78}{space 4} 1.319292{col 91}{space 3} 4.053154
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2}  2.78285{col 50}{space 2} 1.382005{col 61}{space 1}    2.06{col 70}{space 3}0.039{col 78}{space 4} 1.051093{col 91}{space 3}  7.36781
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .3807654{col 50}{space 2} .1290625{col 61}{space 1}   -2.85{col 70}{space 3}0.004{col 78}{space 4}  .195907{col 91}{space 3} .7400566
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .3380461{col 50}{space 2} .0996209{col 61}{space 1}   -3.68{col 70}{space 3}0.000{col 78}{space 4} .1896939{col 91}{space 3} .6024189
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .2992312{col 50}{space 2} .0888934{col 61}{space 1}   -4.06{col 70}{space 3}0.000{col 78}{space 4} .1671325{col 91}{space 3} .5357385
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .3015157{col 50}{space 2} .0876506{col 61}{space 1}   -4.12{col 70}{space 3}0.000{col 78}{space 4} .1705257{col 91}{space 3}  .533126
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}  .677761{col 50}{space 2} .1575544{col 61}{space 1}   -1.67{col 70}{space 3}0.094{col 78}{space 4}  .429678{col 91}{space 3}  1.06908
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} .4170093{col 50}{space 2} .1227577{col 61}{space 1}   -2.97{col 70}{space 3}0.003{col 78}{space 4} .2341507{col 91}{space 3} .7426702
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .5801413{col 50}{space 2} .1648195{col 61}{space 1}   -1.92{col 70}{space 3}0.055{col 78}{space 4} .3323774{col 91}{space 3} 1.012596
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}  .192826{col 50}{space 2} .0788054{col 61}{space 1}   -4.03{col 70}{space 3}0.000{col 78}{space 4} .0865335{col 91}{space 3} .4296815
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .0691097{col 50}{space 2} .0234731{col 61}{space 1}   -7.87{col 70}{space 3}0.000{col 78}{space 4} .0355091{col 91}{space 3} .1345047
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit immigration_improve ib3.age_3 $controls_demographics $controls_objective if infullmodel_TableD4 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      6.65
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                 immigration_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}age_3group {c |}
{space 30}18-29  {c |}{col 38}{res}{space 2} 4.627784{col 50}{space 2} 1.232217{col 61}{space 1}    5.75{col 70}{space 3}0.000{col 78}{space 4} 2.745736{col 91}{space 3} 7.799873
{txt}{space 30}30-49  {c |}{col 38}{res}{space 2} 2.522182{col 50}{space 2} .5757908{col 61}{space 1}    4.05{col 70}{space 3}0.000{col 78}{space 4} 1.612114{col 91}{space 3} 3.946002
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6327077{col 50}{space 2} .1066214{col 61}{space 1}   -2.72{col 70}{space 3}0.007{col 78}{space 4} .4546929{col 91}{space 3} .8804165
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  1.59962{col 50}{space 2} .2886746{col 61}{space 1}    2.60{col 70}{space 3}0.009{col 78}{space 4} 1.122945{col 91}{space 3} 2.278639
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .8750304{col 50}{space 2} .2127652{col 61}{space 1}   -0.55{col 70}{space 3}0.583{col 78}{space 4} .5432362{col 91}{space 3} 1.409476
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.530704{col 50}{space 2} .2892899{col 61}{space 1}    2.25{col 70}{space 3}0.024{col 78}{space 4}  1.05675{col 91}{space 3} 2.217227
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}   1.5984{col 50}{space 2} .5418812{col 61}{space 1}    1.38{col 70}{space 3}0.167{col 78}{space 4} .8222948{col 91}{space 3} 3.107017
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 2.379369{col 50}{space 2} .6929929{col 61}{space 1}    2.98{col 70}{space 3}0.003{col 78}{space 4} 1.344228{col 91}{space 3} 4.211634
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 2.843035{col 50}{space 2} 1.420095{col 61}{space 1}    2.09{col 70}{space 3}0.037{col 78}{space 4} 1.067769{col 91}{space 3} 7.569848
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .3855199{col 50}{space 2} .1295655{col 61}{space 1}   -2.84{col 70}{space 3}0.005{col 78}{space 4} .1994746{col 91}{space 3} .7450853
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .3391026{col 50}{space 2}   .10098{col 61}{space 1}   -3.63{col 70}{space 3}0.000{col 78}{space 4} .1891376{col 91}{space 3} .6079734
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .2984927{col 50}{space 2} .0892649{col 61}{space 1}   -4.04{col 70}{space 3}0.000{col 78}{space 4} .1660742{col 91}{space 3} .5364944
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .2989838{col 50}{space 2} .0867787{col 61}{space 1}   -4.16{col 70}{space 3}0.000{col 78}{space 4} .1692446{col 91}{space 3} .5281783
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .6383978{col 50}{space 2}  .155782{col 61}{space 1}   -1.84{col 70}{space 3}0.066{col 78}{space 4} .3956557{col 91}{space 3} 1.030067
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} .5915131{col 50}{space 2} .2114711{col 61}{space 1}   -1.47{col 70}{space 3}0.142{col 78}{space 4} .2934671{col 91}{space 3} 1.192256
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .6668142{col 50}{space 2} .2494675{col 61}{space 1}   -1.08{col 70}{space 3}0.279{col 78}{space 4} .3202265{col 91}{space 3} 1.388521
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .2398477{col 50}{space 2} .1187657{col 61}{space 1}   -2.88{col 70}{space 3}0.004{col 78}{space 4} .0908481{col 91}{space 3} .6332204
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.153029{col 50}{space 2} .2352344{col 61}{space 1}    0.70{col 70}{space 3}0.485{col 78}{space 4}  .772912{col 91}{space 3} 1.720088
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} .9666115{col 50}{space 2} .4463419{col 61}{space 1}   -0.07{col 70}{space 3}0.941{col 78}{space 4} .3909143{col 91}{space 3} 2.390134
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} .9426562{col 50}{space 2} .4153522{col 61}{space 1}   -0.13{col 70}{space 3}0.893{col 78}{space 4} .3973591{col 91}{space 3} 2.236266
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .5014466{col 50}{space 2} .2101865{col 61}{space 1}   -1.65{col 70}{space 3}0.100{col 78}{space 4}  .220458{col 91}{space 3} 1.140574
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .6785262{col 50}{space 2} .1817789{col 61}{space 1}   -1.45{col 70}{space 3}0.148{col 78}{space 4} .4012896{col 91}{space 3} 1.147296
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.120495{col 50}{space 2} .2439613{col 61}{space 1}    0.52{col 70}{space 3}0.601{col 78}{space 4} .7311801{col 91}{space 3}   1.7171
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .0639855{col 50}{space 2}  .030462{col 61}{space 1}   -5.77{col 70}{space 3}0.000{col 78}{space 4} .0251606{col 91}{space 3} .1627204
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 3: Worsen v Improve
. svy: logit immigration_directionworse ib3.age_3 if infullmodel_TableD4 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       907
{txt}{col 1}Number of PSUs{col 20}= {res}      907{txt}{col 49}Population size{col 67}={res} 1,006.7824
{txt}{col 49}Design df{col 67}= {res}       906
{txt}{col 49}F({res}   2{txt},{res}    905{txt}){col 67}= {res}     26.41
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}  Linearized
{col 1}immigration_directionworse{col 28}{c |} Odds Ratio{col 40}   Std. Err.{col 52}      t{col 60}   P>|t|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 16}age_3group {c |}
{space 20}18-29  {c |}{col 28}{res}{space 2} .1323379{col 40}{space 2} .0370747{col 51}{space 1}   -7.22{col 60}{space 3}0.000{col 68}{space 4} .0763659{col 81}{space 3} .2293341
{txt}{space 20}30-49  {c |}{col 28}{res}{space 2} .3340793{col 40}{space 2} .0749824{col 51}{space 1}   -4.88{col 60}{space 3}0.000{col 68}{space 4} .2150533{col 81}{space 3} .5189827
{txt}{space 26} {c |}
{space 21}_cons {c |}{col 28}{res}{space 2}  9.98252{col 40}{space 2} 1.903446{col 51}{space 1}   12.07{col 60}{space 3}0.000{col 68}{space 4}  6.86622{col 81}{space 3} 14.51318
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit immigration_directionworse ib3.age_3 $controls_demographics if infullmodel_TableD4 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       907
{txt}{col 1}Number of PSUs{col 20}= {res}      907{txt}{col 49}Population size{col 67}={res} 1,006.7824
{txt}{col 49}Design df{col 67}= {res}       906
{txt}{col 49}F({res}  17{txt},{res}    890{txt}){col 67}= {res}      8.38
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}          immigration_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}age_3group {c |}
{space 30}18-29  {c |}{col 38}{res}{space 2} .1395353{col 50}{space 2} .0432208{col 61}{space 1}   -6.36{col 70}{space 3}0.000{col 78}{space 4} .0759755{col 91}{space 3} .2562682
{txt}{space 30}30-49  {c |}{col 38}{res}{space 2} .3526344{col 50}{space 2} .0885269{col 61}{space 1}   -4.15{col 70}{space 3}0.000{col 78}{space 4} .2154515{col 91}{space 3} .5771648
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .8566872{col 50}{space 2} .1800572{col 61}{space 1}   -0.74{col 70}{space 3}0.462{col 78}{space 4} .5671249{col 91}{space 3} 1.294094
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .5189287{col 50}{space 2} .1065262{col 61}{space 1}   -3.20{col 70}{space 3}0.001{col 78}{space 4} .3468474{col 91}{space 3} .7763846
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.018112{col 50}{space 2} .2694679{col 61}{space 1}    0.07{col 70}{space 3}0.946{col 78}{space 4}  .605624{col 91}{space 3} 1.711543
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .6242586{col 50}{space 2}   .13483{col 61}{space 1}   -2.18{col 70}{space 3}0.029{col 78}{space 4} .4085756{col 91}{space 3} .9537984
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}  .373052{col 50}{space 2} .1877147{col 61}{space 1}   -1.96{col 70}{space 3}0.050{col 78}{space 4}  .138958{col 91}{space 3}  1.00151
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .3478355{col 50}{space 2} .1153555{col 61}{space 1}   -3.18{col 70}{space 3}0.002{col 78}{space 4} .1814284{col 91}{space 3}  .666872
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .1620861{col 50}{space 2} .1142552{col 61}{space 1}   -2.58{col 70}{space 3}0.010{col 78}{space 4} .0406376{col 91}{space 3} .6464936
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.480791{col 50}{space 2} .5637549{col 61}{space 1}    1.03{col 70}{space 3}0.303{col 78}{space 4} .7014516{col 91}{space 3} 3.126007
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.469768{col 50}{space 2} .5058497{col 61}{space 1}    1.12{col 70}{space 3}0.263{col 78}{space 4} .7479959{col 91}{space 3} 2.888009
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.895567{col 50}{space 2} .6429994{col 61}{space 1}    1.89{col 70}{space 3}0.060{col 78}{space 4} .9741258{col 91}{space 3} 3.688615
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.503835{col 50}{space 2}  .508504{col 61}{space 1}    1.21{col 70}{space 3}0.228{col 78}{space 4} .7744465{col 91}{space 3} 2.920175
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.231503{col 50}{space 2} .3397895{col 61}{space 1}    0.75{col 70}{space 3}0.451{col 78}{space 4} .7165767{col 91}{space 3} 2.116453
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}  3.19873{col 50}{space 2} 1.102565{col 61}{space 1}    3.37{col 70}{space 3}0.001{col 78}{space 4} 1.626245{col 91}{space 3}  6.29172
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.799199{col 50}{space 2} .6013717{col 61}{space 1}    1.76{col 70}{space 3}0.079{col 78}{space 4} .9336613{col 91}{space 3}  3.46712
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}  7.46925{col 50}{space 2} 2.924152{col 61}{space 1}    5.14{col 70}{space 3}0.000{col 78}{space 4} 3.464117{col 91}{space 3} 16.10503
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 8.523533{col 50}{space 2} 3.447281{col 61}{space 1}    5.30{col 70}{space 3}0.000{col 78}{space 4} 3.853868{col 91}{space 3} 18.85135
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit immigration_directionworse ib3.age_3 $controls_demographics $controls_objective if infullmodel_TableD4 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       907
{txt}{col 1}Number of PSUs{col 20}= {res}      907{txt}{col 49}Population size{col 67}={res} 1,006.7824
{txt}{col 49}Design df{col 67}= {res}       906
{txt}{col 49}F({res}  23{txt},{res}    884{txt}){col 67}= {res}      6.72
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}          immigration_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}age_3group {c |}
{space 30}18-29  {c |}{col 38}{res}{space 2} .1290852{col 50}{space 2} .0403642{col 61}{space 1}   -6.55{col 70}{space 3}0.000{col 78}{space 4} .0698803{col 91}{space 3} .2384506
{txt}{space 30}30-49  {c |}{col 38}{res}{space 2} .3303893{col 50}{space 2} .0818483{col 61}{space 1}   -4.47{col 70}{space 3}0.000{col 78}{space 4} .2031763{col 91}{space 3} .5372529
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .8649187{col 50}{space 2} .1869625{col 61}{space 1}   -0.67{col 70}{space 3}0.502{col 78}{space 4} .5658897{col 91}{space 3} 1.321962
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  .516291{col 50}{space 2} .1088358{col 61}{space 1}   -3.14{col 70}{space 3}0.002{col 78}{space 4} .3413647{col 91}{space 3} .7808553
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.032428{col 50}{space 2} .2875799{col 61}{space 1}    0.11{col 70}{space 3}0.909{col 78}{space 4} .5976447{col 91}{space 3} 1.783515
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .5824175{col 50}{space 2}  .127716{col 61}{space 1}   -2.47{col 70}{space 3}0.014{col 78}{space 4} .3787287{col 91}{space 3} .8956549
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .3381561{col 50}{space 2}  .175906{col 61}{space 1}   -2.08{col 70}{space 3}0.037{col 78}{space 4} .1218251{col 91}{space 3} .9386368
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .3337968{col 50}{space 2} .1166511{col 61}{space 1}   -3.14{col 70}{space 3}0.002{col 78}{space 4} .1681191{col 91}{space 3} .6627462
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .1477794{col 50}{space 2} .1025179{col 61}{space 1}   -2.76{col 70}{space 3}0.006{col 78}{space 4} .0378727{col 91}{space 3} .5766358
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}  1.51432{col 50}{space 2} .5844268{col 61}{space 1}    1.08{col 70}{space 3}0.283{col 78}{space 4} .7100206{col 91}{space 3} 3.229716
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.333983{col 50}{space 2}  .476722{col 61}{space 1}    0.81{col 70}{space 3}0.420{col 78}{space 4}  .661533{col 91}{space 3} 2.689979
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.908907{col 50}{space 2}  .665307{col 61}{space 1}    1.86{col 70}{space 3}0.064{col 78}{space 4} .9632094{col 91}{space 3}  3.78311
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.494628{col 50}{space 2} .5094426{col 61}{space 1}    1.18{col 70}{space 3}0.239{col 78}{space 4} .7656208{col 91}{space 3}  2.91778
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.441223{col 50}{space 2} .4333198{col 61}{space 1}    1.22{col 70}{space 3}0.224{col 78}{space 4} .7988503{col 91}{space 3}  2.60014
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 2.484735{col 50}{space 2} 1.243821{col 61}{space 1}    1.82{col 70}{space 3}0.069{col 78}{space 4}  .930275{col 91}{space 3} 6.636646
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.433121{col 50}{space 2} .5973314{col 61}{space 1}    0.86{col 70}{space 3}0.388{col 78}{space 4} .6324459{col 91}{space 3} 3.247448
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 5.020165{col 50}{space 2} 2.570767{col 61}{space 1}    3.15{col 70}{space 3}0.002{col 78}{space 4} 1.837574{col 91}{space 3} 13.71486
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .6914669{col 50}{space 2} .1766033{col 61}{space 1}   -1.44{col 70}{space 3}0.149{col 78}{space 4} .4188712{col 91}{space 3} 1.141464
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.845413{col 50}{space 2} .9589806{col 61}{space 1}    1.18{col 70}{space 3}0.239{col 78}{space 4} .6655332{col 91}{space 3} 5.117026
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.537274{col 50}{space 2} .7708171{col 61}{space 1}    0.86{col 70}{space 3}0.391{col 78}{space 4}  .574609{col 91}{space 3} 4.112729
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2}  1.39818{col 50}{space 2} .8245047{col 61}{space 1}    0.57{col 70}{space 3}0.570{col 78}{space 4} .4394799{col 91}{space 3} 4.448226
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.470139{col 50}{space 2} .4711989{col 61}{space 1}    1.20{col 70}{space 3}0.230{col 78}{space 4} .7837406{col 91}{space 3} 2.757685
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} .5774212{col 50}{space 2} .1611051{col 61}{space 1}   -1.97{col 70}{space 3}0.049{col 78}{space 4} .3339512{col 91}{space 3} .9983952
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 9.144727{col 50}{space 2} 5.043275{col 61}{space 1}    4.01{col 70}{space 3}0.000{col 78}{space 4} 3.098197{col 91}{space 3} 26.99184
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. 
. ******* Gender ********
. 
. * Model 1: Worsen 
. svy: logit immigration_worsen ib1.female_dummy if infullmodel_TableD4 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   1{txt},{res}   3962{txt}){col 67}= {res}     41.13
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}  Linearized
{col 1}immigration_worsen{col 20}{c |} Odds Ratio{col 32}   Std. Err.{col 44}      t{col 52}   P>|t|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}female_dummy {c |}
{space 13}Male  {c |}{col 20}{res}{space 2} 1.788371{col 32}{space 2} .1620991{col 43}{space 1}    6.41{col 52}{space 3}0.000{col 60}{space 4} 1.497203{col 73}{space 3} 2.136164
{txt}{space 13}_cons {c |}{col 20}{res}{space 2} .1753445{col 32}{space 2} .0113195{col 43}{space 1}  -26.97{col 52}{space 3}0.000{col 60}{space 4} .1544989{col 73}{space 3} .1990028
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit immigration_worsen ib1.female_dummy $controls_demographics if infullmodel_TableD4 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      7.86
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                  immigration_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.678414{col 50}{space 2} .1636046{col 61}{space 1}    5.31{col 70}{space 3}0.000{col 78}{space 4} 1.386444{col 91}{space 3} 2.031871
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.492692{col 50}{space 2} .2349607{col 61}{space 1}    2.54{col 70}{space 3}0.011{col 78}{space 4} 1.096336{col 91}{space 3}  2.03234
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.610056{col 50}{space 2} .2627392{col 61}{space 1}    2.92{col 70}{space 3}0.004{col 78}{space 4} 1.169214{col 91}{space 3} 2.217113
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7248211{col 50}{space 2}  .076892{col 61}{space 1}   -3.03{col 70}{space 3}0.002{col 78}{space 4} .5887139{col 91}{space 3} .8923955
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.042172{col 50}{space 2} .1260904{col 61}{space 1}    0.34{col 70}{space 3}0.733{col 78}{space 4} .8220966{col 91}{space 3} 1.321163
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .8324747{col 50}{space 2} .0880657{col 61}{space 1}   -1.73{col 70}{space 3}0.083{col 78}{space 4} .6765451{col 91}{space 3} 1.024343
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .5974396{col 50}{space 2} .1563604{col 61}{space 1}   -1.97{col 70}{space 3}0.049{col 78}{space 4} .3576446{col 91}{space 3} .9980136
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .7180797{col 50}{space 2}  .123263{col 61}{space 1}   -1.93{col 70}{space 3}0.054{col 78}{space 4} .5128777{col 91}{space 3} 1.005383
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5878524{col 50}{space 2} .1865065{col 61}{space 1}   -1.67{col 70}{space 3}0.094{col 78}{space 4} .3155928{col 91}{space 3} 1.094988
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .6709222{col 50}{space 2} .1149382{col 61}{space 1}   -2.33{col 70}{space 3}0.020{col 78}{space 4} .4795182{col 91}{space 3} .9387268
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .7103922{col 50}{space 2} .1180463{col 61}{space 1}   -2.06{col 70}{space 3}0.040{col 78}{space 4} .5128735{col 91}{space 3} .9839796
{txt}{space 34}4  {c |}{col 38}{res}{space 2}  .780437{col 50}{space 2} .1288428{col 61}{space 1}   -1.50{col 70}{space 3}0.133{col 78}{space 4} .5646373{col 91}{space 3} 1.078713
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .5720486{col 50}{space 2}  .100061{col 61}{space 1}   -3.19{col 70}{space 3}0.001{col 78}{space 4} .4059736{col 91}{space 3} .8060612
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .8561006{col 50}{space 2} .1220303{col 61}{space 1}   -1.09{col 70}{space 3}0.276{col 78}{space 4} .6473746{col 91}{space 3} 1.132124
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.278422{col 50}{space 2} .1836731{col 61}{space 1}    1.71{col 70}{space 3}0.087{col 78}{space 4} .9645912{col 91}{space 3} 1.694358
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.037038{col 50}{space 2} .1644867{col 61}{space 1}    0.23{col 70}{space 3}0.819{col 78}{space 4} .7598757{col 91}{space 3} 1.415294
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}   1.7023{col 50}{space 2} .2648454{col 61}{space 1}    3.42{col 70}{space 3}0.001{col 78}{space 4} 1.254772{col 91}{space 3} 2.309443
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .1999218{col 50}{space 2} .0448009{col 61}{space 1}   -7.18{col 70}{space 3}0.000{col 78}{space 4} .1288411{col 91}{space 3} .3102172
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit immigration_worsen ib1.female_dummy $controls_demographics  $controls_objective if infullmodel_TableD4 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      6.22
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                  immigration_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.667937{col 50}{space 2} .1635652{col 61}{space 1}    5.22{col 70}{space 3}0.000{col 78}{space 4}   1.3762{col 91}{space 3} 2.021519
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.494482{col 50}{space 2} .2352497{col 61}{space 1}    2.55{col 70}{space 3}0.011{col 78}{space 4} 1.097641{col 91}{space 3} 2.034797
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.610733{col 50}{space 2} .2628841{col 61}{space 1}    2.92{col 70}{space 3}0.004{col 78}{space 4} 1.169657{col 91}{space 3} 2.218139
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7254971{col 50}{space 2} .0774474{col 61}{space 1}   -3.01{col 70}{space 3}0.003{col 78}{space 4} .5884933{col 91}{space 3} .8943961
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.018511{col 50}{space 2} .1252356{col 61}{space 1}    0.15{col 70}{space 3}0.881{col 78}{space 4} .8003321{col 91}{space 3} 1.296167
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2}  .822743{col 50}{space 2} .0896053{col 61}{space 1}   -1.79{col 70}{space 3}0.073{col 78}{space 4} .6645552{col 91}{space 3} 1.018585
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .6033868{col 50}{space 2} .1581631{col 61}{space 1}   -1.93{col 70}{space 3}0.054{col 78}{space 4} .3609159{col 91}{space 3} 1.008755
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .7067927{col 50}{space 2} .1216358{col 61}{space 1}   -2.02{col 70}{space 3}0.044{col 78}{space 4} .5043818{col 91}{space 3} .9904322
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5794473{col 50}{space 2} .1826008{col 61}{space 1}   -1.73{col 70}{space 3}0.083{col 78}{space 4} .3123873{col 91}{space 3} 1.074817
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .6693483{col 50}{space 2} .1154689{col 61}{space 1}   -2.33{col 70}{space 3}0.020{col 78}{space 4} .4772732{col 91}{space 3} .9387227
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .7129928{col 50}{space 2} .1187527{col 61}{space 1}   -2.03{col 70}{space 3}0.042{col 78}{space 4} .5143631{col 91}{space 3} .9883268
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .7970378{col 50}{space 2} .1319035{col 61}{space 1}   -1.37{col 70}{space 3}0.171{col 78}{space 4}  .576194{col 91}{space 3} 1.102527
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .5898411{col 50}{space 2} .1036875{col 61}{space 1}   -3.00{col 70}{space 3}0.003{col 78}{space 4} .4178858{col 91}{space 3} .8325541
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .8419726{col 50}{space 2} .1299202{col 61}{space 1}   -1.11{col 70}{space 3}0.265{col 78}{space 4} .6221765{col 91}{space 3} 1.139416
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.219233{col 50}{space 2} .2865309{col 61}{space 1}    0.84{col 70}{space 3}0.399{col 78}{space 4}  .769105{col 91}{space 3} 1.932805
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .8823133{col 50}{space 2} .1578064{col 61}{space 1}   -0.70{col 70}{space 3}0.484{col 78}{space 4} .6213473{col 91}{space 3} 1.252885
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.365186{col 50}{space 2} .2966848{col 61}{space 1}    1.43{col 70}{space 3}0.152{col 78}{space 4} .8915596{col 91}{space 3} 2.090417
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .7326398{col 50}{space 2}   .08772{col 61}{space 1}   -2.60{col 70}{space 3}0.009{col 78}{space 4} .5793538{col 91}{space 3} .9264825
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.905536{col 50}{space 2} .4550764{col 61}{space 1}    2.70{col 70}{space 3}0.007{col 78}{space 4} 1.193089{col 91}{space 3} 3.043418
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2}  1.46295{col 50}{space 2} .3511115{col 61}{space 1}    1.59{col 70}{space 3}0.113{col 78}{space 4} .9138537{col 91}{space 3} 2.341975
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .8878147{col 50}{space 2} .2416446{col 61}{space 1}   -0.44{col 70}{space 3}0.662{col 78}{space 4} .5206817{col 91}{space 3} 1.513814
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.083534{col 50}{space 2} .1647442{col 61}{space 1}    0.53{col 70}{space 3}0.598{col 78}{space 4} .8042362{col 91}{space 3} 1.459828
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} .7759497{col 50}{space 2} .1117268{col 61}{space 1}   -1.76{col 70}{space 3}0.078{col 78}{space 4} .5851052{col 91}{space 3} 1.029042
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2}  .177409{col 50}{space 2} .0482174{col 61}{space 1}   -6.36{col 70}{space 3}0.000{col 78}{space 4} .1041261{col 91}{space 3} .3022676
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 2: Improve 
. svy: logit immigration_improve ib1.female_dummy if infullmodel_TableD4 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   1{txt},{res}   3962{txt}){col 67}= {res}      4.05
{txt}{col 49}Prob > F{col 67}= {res}    0.0442

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}  Linearized
{col 1}immigration_improve{col 21}{c |} Odds Ratio{col 33}   Std. Err.{col 45}      t{col 53}   P>|t|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}female_dummy {c |}
{space 14}Male  {c |}{col 21}{res}{space 2} 1.377544{col 33}{space 2}  .219237{col 44}{space 1}    2.01{col 53}{space 3}0.044{col 61}{space 4} 1.008312{col 74}{space 3} 1.881985
{txt}{space 14}_cons {c |}{col 21}{res}{space 2} .0465162{col 33}{space 2} .0053367{col 44}{space 1}  -26.74{col 53}{space 3}0.000{col 61}{space 4} .0371465{col 74}{space 3} .0582493
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit immigration_improve ib1.female_dummy $controls_demographics if infullmodel_TableD4 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      8.18
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                 immigration_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.597082{col 50}{space 2}  .265851{col 61}{space 1}    2.81{col 70}{space 3}0.005{col 78}{space 4} 1.152372{col 91}{space 3}  2.21341
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .5238864{col 50}{space 2} .1042154{col 61}{space 1}   -3.25{col 70}{space 3}0.001{col 78}{space 4} .3546975{col 91}{space 3} .7737776
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .2096789{col 50}{space 2}  .055851{col 61}{space 1}   -5.86{col 70}{space 3}0.000{col 78}{space 4} .1243815{col 91}{space 3} .3534709
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.582045{col 50}{space 2} .2810231{col 61}{space 1}    2.58{col 70}{space 3}0.010{col 78}{space 4} 1.116789{col 91}{space 3} 2.241126
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .8706222{col 50}{space 2} .2078136{col 61}{space 1}   -0.58{col 70}{space 3}0.562{col 78}{space 4} .5452426{col 91}{space 3} 1.390176
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.493342{col 50}{space 2} .2813514{col 61}{space 1}    2.13{col 70}{space 3}0.033{col 78}{space 4} 1.032144{col 91}{space 3} 2.160617
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.580833{col 50}{space 2} .5289845{col 61}{space 1}    1.37{col 70}{space 3}0.171{col 78}{space 4} .8202885{col 91}{space 3}  3.04653
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 2.312422{col 50}{space 2} .6619176{col 61}{space 1}    2.93{col 70}{space 3}0.003{col 78}{space 4} 1.319292{col 91}{space 3} 4.053154
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2}  2.78285{col 50}{space 2} 1.382005{col 61}{space 1}    2.06{col 70}{space 3}0.039{col 78}{space 4} 1.051093{col 91}{space 3}  7.36781
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .3807654{col 50}{space 2} .1290625{col 61}{space 1}   -2.85{col 70}{space 3}0.004{col 78}{space 4}  .195907{col 91}{space 3} .7400566
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .3380461{col 50}{space 2} .0996209{col 61}{space 1}   -3.68{col 70}{space 3}0.000{col 78}{space 4} .1896939{col 91}{space 3} .6024189
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .2992312{col 50}{space 2} .0888934{col 61}{space 1}   -4.06{col 70}{space 3}0.000{col 78}{space 4} .1671325{col 91}{space 3} .5357385
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .3015157{col 50}{space 2} .0876506{col 61}{space 1}   -4.12{col 70}{space 3}0.000{col 78}{space 4} .1705257{col 91}{space 3}  .533126
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}  .677761{col 50}{space 2} .1575544{col 61}{space 1}   -1.67{col 70}{space 3}0.094{col 78}{space 4}  .429678{col 91}{space 3}  1.06908
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} .4170093{col 50}{space 2} .1227577{col 61}{space 1}   -2.97{col 70}{space 3}0.003{col 78}{space 4} .2341507{col 91}{space 3} .7426702
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .5801413{col 50}{space 2} .1648195{col 61}{space 1}   -1.92{col 70}{space 3}0.055{col 78}{space 4} .3323774{col 91}{space 3} 1.012596
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}  .192826{col 50}{space 2} .0788054{col 61}{space 1}   -4.03{col 70}{space 3}0.000{col 78}{space 4} .0865335{col 91}{space 3} .4296815
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2063748{col 50}{space 2} .0795952{col 61}{space 1}   -4.09{col 70}{space 3}0.000{col 78}{space 4} .0968864{col 91}{space 3}  .439593
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit immigration_improve ib1.female_dummy $controls_demographics $controls_objective if infullmodel_TableD4 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      6.65
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                 immigration_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.580509{col 50}{space 2} .2663412{col 61}{space 1}    2.72{col 70}{space 3}0.007{col 78}{space 4} 1.135826{col 91}{space 3} 2.199287
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .5450086{col 50}{space 2} .1092392{col 61}{space 1}   -3.03{col 70}{space 3}0.002{col 78}{space 4} .3679086{col 91}{space 3} .8073591
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .2160861{col 50}{space 2} .0575362{col 61}{space 1}   -5.75{col 70}{space 3}0.000{col 78}{space 4} .1282072{col 91}{space 3} .3642011
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  1.59962{col 50}{space 2} .2886746{col 61}{space 1}    2.60{col 70}{space 3}0.009{col 78}{space 4} 1.122945{col 91}{space 3} 2.278639
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .8750304{col 50}{space 2} .2127652{col 61}{space 1}   -0.55{col 70}{space 3}0.583{col 78}{space 4} .5432362{col 91}{space 3} 1.409476
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.530704{col 50}{space 2} .2892899{col 61}{space 1}    2.25{col 70}{space 3}0.024{col 78}{space 4}  1.05675{col 91}{space 3} 2.217227
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}   1.5984{col 50}{space 2} .5418812{col 61}{space 1}    1.38{col 70}{space 3}0.167{col 78}{space 4} .8222948{col 91}{space 3} 3.107017
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 2.379369{col 50}{space 2} .6929929{col 61}{space 1}    2.98{col 70}{space 3}0.003{col 78}{space 4} 1.344228{col 91}{space 3} 4.211634
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 2.843035{col 50}{space 2} 1.420095{col 61}{space 1}    2.09{col 70}{space 3}0.037{col 78}{space 4} 1.067769{col 91}{space 3} 7.569848
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .3855199{col 50}{space 2} .1295655{col 61}{space 1}   -2.84{col 70}{space 3}0.005{col 78}{space 4} .1994746{col 91}{space 3} .7450853
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .3391026{col 50}{space 2}   .10098{col 61}{space 1}   -3.63{col 70}{space 3}0.000{col 78}{space 4} .1891376{col 91}{space 3} .6079734
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .2984927{col 50}{space 2} .0892649{col 61}{space 1}   -4.04{col 70}{space 3}0.000{col 78}{space 4} .1660742{col 91}{space 3} .5364944
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .2989838{col 50}{space 2} .0867787{col 61}{space 1}   -4.16{col 70}{space 3}0.000{col 78}{space 4} .1692446{col 91}{space 3} .5281783
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .6383978{col 50}{space 2}  .155782{col 61}{space 1}   -1.84{col 70}{space 3}0.066{col 78}{space 4} .3956557{col 91}{space 3} 1.030067
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} .5915131{col 50}{space 2} .2114711{col 61}{space 1}   -1.47{col 70}{space 3}0.142{col 78}{space 4} .2934671{col 91}{space 3} 1.192256
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .6668142{col 50}{space 2} .2494675{col 61}{space 1}   -1.08{col 70}{space 3}0.279{col 78}{space 4} .3202265{col 91}{space 3} 1.388521
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .2398477{col 50}{space 2} .1187657{col 61}{space 1}   -2.88{col 70}{space 3}0.004{col 78}{space 4} .0908481{col 91}{space 3} .6332204
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.153029{col 50}{space 2} .2352344{col 61}{space 1}    0.70{col 70}{space 3}0.485{col 78}{space 4}  .772912{col 91}{space 3} 1.720088
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} .9666115{col 50}{space 2} .4463419{col 61}{space 1}   -0.07{col 70}{space 3}0.941{col 78}{space 4} .3909143{col 91}{space 3} 2.390134
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} .9426562{col 50}{space 2} .4153522{col 61}{space 1}   -0.13{col 70}{space 3}0.893{col 78}{space 4} .3973591{col 91}{space 3} 2.236266
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .5014466{col 50}{space 2} .2101865{col 61}{space 1}   -1.65{col 70}{space 3}0.100{col 78}{space 4}  .220458{col 91}{space 3} 1.140574
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .6785262{col 50}{space 2} .1817789{col 61}{space 1}   -1.45{col 70}{space 3}0.148{col 78}{space 4} .4012896{col 91}{space 3} 1.147296
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.120495{col 50}{space 2} .2439613{col 61}{space 1}    0.52{col 70}{space 3}0.601{col 78}{space 4} .7311801{col 91}{space 3}   1.7171
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .1873519{col 50}{space 2} .0918229{col 61}{space 1}   -3.42{col 70}{space 3}0.001{col 78}{space 4} .0716719{col 91}{space 3} .4897419
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 3: Worsen v Improve
. svy: logit immigration_directionworse ib1.female_dummy if infullmodel_TableD4 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       907
{txt}{col 1}Number of PSUs{col 20}= {res}      907{txt}{col 49}Population size{col 67}={res} 1,006.7824
{txt}{col 49}Design df{col 67}= {res}       906
{txt}{col 49}F({res}   1{txt},{res}    906{txt}){col 67}= {res}      0.90
{txt}{col 49}Prob > F{col 67}= {res}    0.3434

{txt}{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}  Linearized
{col 1}immigration_directionworse{col 28}{c |} Odds Ratio{col 40}   Std. Err.{col 52}      t{col 60}   P>|t|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}female_dummy {c |}
{space 21}Male  {c |}{col 28}{res}{space 2} 1.181104{col 40}{space 2} .2073843{col 51}{space 1}    0.95{col 60}{space 3}0.343{col 68}{space 4} .8368155{col 81}{space 3} 1.667041
{txt}{space 21}_cons {c |}{col 28}{res}{space 2} 3.356362{col 40}{space 2} .4270234{col 51}{space 1}    9.52{col 60}{space 3}0.000{col 68}{space 4} 2.614732{col 81}{space 3} 4.308344
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit immigration_directionworse ib1.female_dummy $controls_demographics if infullmodel_TableD4 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       907
{txt}{col 1}Number of PSUs{col 20}= {res}      907{txt}{col 49}Population size{col 67}={res} 1,006.7824
{txt}{col 49}Design df{col 67}= {res}       906
{txt}{col 49}F({res}  17{txt},{res}    890{txt}){col 67}= {res}      8.38
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}          immigration_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.167287{col 50}{space 2} .2453386{col 61}{space 1}    0.74{col 70}{space 3}0.462{col 78}{space 4} .7727414{col 91}{space 3}  1.76328
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 2.527205{col 50}{space 2} .6544328{col 61}{space 1}    3.58{col 70}{space 3}0.000{col 78}{space 4} 1.520277{col 91}{space 3} 4.201055
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 7.166644{col 50}{space 2} 2.219853{col 61}{space 1}    6.36{col 70}{space 3}0.000{col 78}{space 4} 3.902162{col 91}{space 3} 13.16214
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .5189287{col 50}{space 2} .1065262{col 61}{space 1}   -3.20{col 70}{space 3}0.001{col 78}{space 4} .3468474{col 91}{space 3} .7763846
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.018112{col 50}{space 2} .2694679{col 61}{space 1}    0.07{col 70}{space 3}0.946{col 78}{space 4}  .605624{col 91}{space 3} 1.711543
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .6242586{col 50}{space 2}   .13483{col 61}{space 1}   -2.18{col 70}{space 3}0.029{col 78}{space 4} .4085756{col 91}{space 3} .9537984
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}  .373052{col 50}{space 2} .1877147{col 61}{space 1}   -1.96{col 70}{space 3}0.050{col 78}{space 4}  .138958{col 91}{space 3}  1.00151
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .3478355{col 50}{space 2} .1153555{col 61}{space 1}   -3.18{col 70}{space 3}0.002{col 78}{space 4} .1814284{col 91}{space 3}  .666872
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .1620861{col 50}{space 2} .1142552{col 61}{space 1}   -2.58{col 70}{space 3}0.010{col 78}{space 4} .0406376{col 91}{space 3} .6464936
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.480791{col 50}{space 2} .5637549{col 61}{space 1}    1.03{col 70}{space 3}0.303{col 78}{space 4} .7014516{col 91}{space 3} 3.126007
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.469768{col 50}{space 2} .5058497{col 61}{space 1}    1.12{col 70}{space 3}0.263{col 78}{space 4} .7479959{col 91}{space 3} 2.888009
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.895567{col 50}{space 2} .6429994{col 61}{space 1}    1.89{col 70}{space 3}0.060{col 78}{space 4} .9741258{col 91}{space 3} 3.688615
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.503835{col 50}{space 2}  .508504{col 61}{space 1}    1.21{col 70}{space 3}0.228{col 78}{space 4} .7744465{col 91}{space 3} 2.920175
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.231503{col 50}{space 2} .3397895{col 61}{space 1}    0.75{col 70}{space 3}0.451{col 78}{space 4} .7165767{col 91}{space 3} 2.116453
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}  3.19873{col 50}{space 2} 1.102565{col 61}{space 1}    3.37{col 70}{space 3}0.001{col 78}{space 4} 1.626245{col 91}{space 3}  6.29172
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.799199{col 50}{space 2} .6013717{col 61}{space 1}    1.76{col 70}{space 3}0.079{col 78}{space 4} .9336613{col 91}{space 3}  3.46712
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}  7.46925{col 50}{space 2} 2.924152{col 61}{space 1}    5.14{col 70}{space 3}0.000{col 78}{space 4} 3.464117{col 91}{space 3} 16.10503
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 1.018887{col 50}{space 2} .4521175{col 61}{space 1}    0.04{col 70}{space 3}0.966{col 78}{space 4} .4264926{col 91}{space 3} 2.434113
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit immigration_directionworse ib1.female_dummy $controls_demographics $controls_objective if infullmodel_TableD4 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       907
{txt}{col 1}Number of PSUs{col 20}= {res}      907{txt}{col 49}Population size{col 67}={res} 1,006.7824
{txt}{col 49}Design df{col 67}= {res}       906
{txt}{col 49}F({res}  23{txt},{res}    884{txt}){col 67}= {res}      6.72
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}          immigration_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}female_dummy {c |}
{space 31}Male  {c |}{col 38}{res}{space 2} 1.156178{col 50}{space 2} .2499217{col 61}{space 1}    0.67{col 70}{space 3}0.502{col 78}{space 4} .7564517{col 91}{space 3} 1.767129
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 2.559466{col 50}{space 2} .6820355{col 61}{space 1}    3.53{col 70}{space 3}0.000{col 78}{space 4} 1.517125{col 91}{space 3} 4.317947
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 7.746819{col 50}{space 2} 2.422385{col 61}{space 1}    6.55{col 70}{space 3}0.000{col 78}{space 4} 4.193741{col 91}{space 3} 14.31018
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  .516291{col 50}{space 2} .1088358{col 61}{space 1}   -3.14{col 70}{space 3}0.002{col 78}{space 4} .3413647{col 91}{space 3} .7808553
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.032428{col 50}{space 2} .2875799{col 61}{space 1}    0.11{col 70}{space 3}0.909{col 78}{space 4} .5976447{col 91}{space 3} 1.783515
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .5824175{col 50}{space 2}  .127716{col 61}{space 1}   -2.47{col 70}{space 3}0.014{col 78}{space 4} .3787287{col 91}{space 3} .8956549
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .3381561{col 50}{space 2}  .175906{col 61}{space 1}   -2.08{col 70}{space 3}0.037{col 78}{space 4} .1218251{col 91}{space 3} .9386368
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .3337968{col 50}{space 2} .1166511{col 61}{space 1}   -3.14{col 70}{space 3}0.002{col 78}{space 4} .1681191{col 91}{space 3} .6627462
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .1477794{col 50}{space 2} .1025179{col 61}{space 1}   -2.76{col 70}{space 3}0.006{col 78}{space 4} .0378727{col 91}{space 3} .5766358
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}  1.51432{col 50}{space 2} .5844268{col 61}{space 1}    1.08{col 70}{space 3}0.283{col 78}{space 4} .7100206{col 91}{space 3} 3.229716
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.333983{col 50}{space 2}  .476722{col 61}{space 1}    0.81{col 70}{space 3}0.420{col 78}{space 4}  .661533{col 91}{space 3} 2.689979
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.908907{col 50}{space 2}  .665307{col 61}{space 1}    1.86{col 70}{space 3}0.064{col 78}{space 4} .9632094{col 91}{space 3}  3.78311
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.494628{col 50}{space 2} .5094426{col 61}{space 1}    1.18{col 70}{space 3}0.239{col 78}{space 4} .7656208{col 91}{space 3}  2.91778
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.441223{col 50}{space 2} .4333198{col 61}{space 1}    1.22{col 70}{space 3}0.224{col 78}{space 4} .7988503{col 91}{space 3}  2.60014
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 2.484735{col 50}{space 2} 1.243821{col 61}{space 1}    1.82{col 70}{space 3}0.069{col 78}{space 4}  .930275{col 91}{space 3} 6.636646
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.433121{col 50}{space 2} .5973314{col 61}{space 1}    0.86{col 70}{space 3}0.388{col 78}{space 4} .6324459{col 91}{space 3} 3.247448
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 5.020165{col 50}{space 2} 2.570767{col 61}{space 1}    3.15{col 70}{space 3}0.002{col 78}{space 4} 1.837574{col 91}{space 3} 13.71486
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .6914669{col 50}{space 2} .1766033{col 61}{space 1}   -1.44{col 70}{space 3}0.149{col 78}{space 4} .4188712{col 91}{space 3} 1.141464
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.845413{col 50}{space 2} .9589806{col 61}{space 1}    1.18{col 70}{space 3}0.239{col 78}{space 4} .6655332{col 91}{space 3} 5.117026
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.537274{col 50}{space 2} .7708171{col 61}{space 1}    0.86{col 70}{space 3}0.391{col 78}{space 4}  .574609{col 91}{space 3} 4.112729
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2}  1.39818{col 50}{space 2} .8245047{col 61}{space 1}    0.57{col 70}{space 3}0.570{col 78}{space 4} .4394799{col 91}{space 3} 4.448226
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.470139{col 50}{space 2} .4711989{col 61}{space 1}    1.20{col 70}{space 3}0.230{col 78}{space 4} .7837406{col 91}{space 3} 2.757685
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} .5774212{col 50}{space 2} .1611051{col 61}{space 1}   -1.97{col 70}{space 3}0.049{col 78}{space 4} .3339512{col 91}{space 3} .9983952
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 1.020993{col 50}{space 2} .5653201{col 61}{space 1}    0.04{col 70}{space 3}0.970{col 78}{space 4} .3444172{col 91}{space 3} 3.026638
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. 
. ******* Education ********
. 
. * Model 1: Worsen 
. svy: logit immigration_worsen i.degree_dummy if infullmodel_TableD4 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   1{txt},{res}   3962{txt}){col 67}= {res}     36.31
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}  Linearized
{col 1}immigration_worsen{col 20}{c |} Odds Ratio{col 32}   Std. Err.{col 44}      t{col 52}   P>|t|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}degree_dummy {c |}
{space 4}Degree-Holder  {c |}{col 20}{res}{space 2} .5649611{col 32}{space 2} .0535352{col 43}{space 1}   -6.03{col 52}{space 3}0.000{col 60}{space 4}  .469175{col 73}{space 3} .6803028
{txt}{space 13}_cons {c |}{col 20}{res}{space 2} .3039909{col 32}{space 2} .0177464{col 43}{space 1}  -20.40{col 52}{space 3}0.000{col 60}{space 4} .2711152{col 73}{space 3} .3408532
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit immigration_worsen i.degree_dummy $controls_demographics if infullmodel_TableD4 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      7.86
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                  immigration_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7248211{col 50}{space 2}  .076892{col 61}{space 1}   -3.03{col 70}{space 3}0.002{col 78}{space 4} .5887139{col 91}{space 3} .8923955
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.492692{col 50}{space 2} .2349607{col 61}{space 1}    2.54{col 70}{space 3}0.011{col 78}{space 4} 1.096336{col 91}{space 3}  2.03234
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.610056{col 50}{space 2} .2627392{col 61}{space 1}    2.92{col 70}{space 3}0.004{col 78}{space 4} 1.169214{col 91}{space 3} 2.217113
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .5958004{col 50}{space 2} .0580761{col 61}{space 1}   -5.31{col 70}{space 3}0.000{col 78}{space 4} .4921573{col 91}{space 3} .7212696
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.042172{col 50}{space 2} .1260904{col 61}{space 1}    0.34{col 70}{space 3}0.733{col 78}{space 4} .8220966{col 91}{space 3} 1.321163
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .8324747{col 50}{space 2} .0880657{col 61}{space 1}   -1.73{col 70}{space 3}0.083{col 78}{space 4} .6765451{col 91}{space 3} 1.024343
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .5974396{col 50}{space 2} .1563604{col 61}{space 1}   -1.97{col 70}{space 3}0.049{col 78}{space 4} .3576446{col 91}{space 3} .9980136
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .7180797{col 50}{space 2}  .123263{col 61}{space 1}   -1.93{col 70}{space 3}0.054{col 78}{space 4} .5128777{col 91}{space 3} 1.005383
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5878524{col 50}{space 2} .1865065{col 61}{space 1}   -1.67{col 70}{space 3}0.094{col 78}{space 4} .3155928{col 91}{space 3} 1.094988
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .6709222{col 50}{space 2} .1149382{col 61}{space 1}   -2.33{col 70}{space 3}0.020{col 78}{space 4} .4795182{col 91}{space 3} .9387268
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .7103922{col 50}{space 2} .1180463{col 61}{space 1}   -2.06{col 70}{space 3}0.040{col 78}{space 4} .5128735{col 91}{space 3} .9839796
{txt}{space 34}4  {c |}{col 38}{res}{space 2}  .780437{col 50}{space 2} .1288428{col 61}{space 1}   -1.50{col 70}{space 3}0.133{col 78}{space 4} .5646373{col 91}{space 3} 1.078713
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .5720486{col 50}{space 2}  .100061{col 61}{space 1}   -3.19{col 70}{space 3}0.001{col 78}{space 4} .4059736{col 91}{space 3} .8060612
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .8561006{col 50}{space 2} .1220303{col 61}{space 1}   -1.09{col 70}{space 3}0.276{col 78}{space 4} .6473746{col 91}{space 3} 1.132124
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.278422{col 50}{space 2} .1836731{col 61}{space 1}    1.71{col 70}{space 3}0.087{col 78}{space 4} .9645912{col 91}{space 3} 1.694358
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.037038{col 50}{space 2} .1644867{col 61}{space 1}    0.23{col 70}{space 3}0.819{col 78}{space 4} .7598757{col 91}{space 3} 1.415294
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}   1.7023{col 50}{space 2} .2648454{col 61}{space 1}    3.42{col 70}{space 3}0.001{col 78}{space 4} 1.254772{col 91}{space 3} 2.309443
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .3355516{col 50}{space 2} .0770668{col 61}{space 1}   -4.75{col 70}{space 3}0.000{col 78}{space 4} .2138958{col 91}{space 3} .5264005
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit immigration_worsen i.degree_dummy $controls_demographics  $controls_objective if infullmodel_TableD4 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      6.22
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                  immigration_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7254971{col 50}{space 2} .0774474{col 61}{space 1}   -3.01{col 70}{space 3}0.003{col 78}{space 4} .5884933{col 91}{space 3} .8943961
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.494482{col 50}{space 2} .2352497{col 61}{space 1}    2.55{col 70}{space 3}0.011{col 78}{space 4} 1.097641{col 91}{space 3} 2.034797
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.610733{col 50}{space 2} .2628841{col 61}{space 1}    2.92{col 70}{space 3}0.004{col 78}{space 4} 1.169657{col 91}{space 3} 2.218139
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}  .599543{col 50}{space 2} .0587938{col 61}{space 1}   -5.22{col 70}{space 3}0.000{col 78}{space 4} .4946776{col 91}{space 3} .7266385
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.018511{col 50}{space 2} .1252356{col 61}{space 1}    0.15{col 70}{space 3}0.881{col 78}{space 4} .8003321{col 91}{space 3} 1.296167
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2}  .822743{col 50}{space 2} .0896053{col 61}{space 1}   -1.79{col 70}{space 3}0.073{col 78}{space 4} .6645552{col 91}{space 3} 1.018585
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .6033868{col 50}{space 2} .1581631{col 61}{space 1}   -1.93{col 70}{space 3}0.054{col 78}{space 4} .3609159{col 91}{space 3} 1.008755
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .7067927{col 50}{space 2} .1216358{col 61}{space 1}   -2.02{col 70}{space 3}0.044{col 78}{space 4} .5043818{col 91}{space 3} .9904322
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5794473{col 50}{space 2} .1826008{col 61}{space 1}   -1.73{col 70}{space 3}0.083{col 78}{space 4} .3123873{col 91}{space 3} 1.074817
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .6693483{col 50}{space 2} .1154689{col 61}{space 1}   -2.33{col 70}{space 3}0.020{col 78}{space 4} .4772732{col 91}{space 3} .9387227
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .7129928{col 50}{space 2} .1187527{col 61}{space 1}   -2.03{col 70}{space 3}0.042{col 78}{space 4} .5143631{col 91}{space 3} .9883268
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .7970378{col 50}{space 2} .1319035{col 61}{space 1}   -1.37{col 70}{space 3}0.171{col 78}{space 4}  .576194{col 91}{space 3} 1.102527
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .5898411{col 50}{space 2} .1036875{col 61}{space 1}   -3.00{col 70}{space 3}0.003{col 78}{space 4} .4178858{col 91}{space 3} .8325541
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .8419726{col 50}{space 2} .1299202{col 61}{space 1}   -1.11{col 70}{space 3}0.265{col 78}{space 4} .6221765{col 91}{space 3} 1.139416
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.219233{col 50}{space 2} .2865309{col 61}{space 1}    0.84{col 70}{space 3}0.399{col 78}{space 4}  .769105{col 91}{space 3} 1.932805
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .8823133{col 50}{space 2} .1578064{col 61}{space 1}   -0.70{col 70}{space 3}0.484{col 78}{space 4} .6213473{col 91}{space 3} 1.252885
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.365186{col 50}{space 2} .2966848{col 61}{space 1}    1.43{col 70}{space 3}0.152{col 78}{space 4} .8915596{col 91}{space 3} 2.090417
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .7326398{col 50}{space 2}   .08772{col 61}{space 1}   -2.60{col 70}{space 3}0.009{col 78}{space 4} .5793538{col 91}{space 3} .9264825
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.905536{col 50}{space 2} .4550764{col 61}{space 1}    2.70{col 70}{space 3}0.007{col 78}{space 4} 1.193089{col 91}{space 3} 3.043418
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2}  1.46295{col 50}{space 2} .3511115{col 61}{space 1}    1.59{col 70}{space 3}0.113{col 78}{space 4} .9138537{col 91}{space 3} 2.341975
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .8878147{col 50}{space 2} .2416446{col 61}{space 1}   -0.44{col 70}{space 3}0.662{col 78}{space 4} .5206817{col 91}{space 3} 1.513814
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.083534{col 50}{space 2} .1647442{col 61}{space 1}    0.53{col 70}{space 3}0.598{col 78}{space 4} .8042362{col 91}{space 3} 1.459828
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} .7759497{col 50}{space 2} .1117268{col 61}{space 1}   -1.76{col 70}{space 3}0.078{col 78}{space 4} .5851052{col 91}{space 3} 1.029042
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2}  .295907{col 50}{space 2} .0809559{col 61}{space 1}   -4.45{col 70}{space 3}0.000{col 78}{space 4} .1730643{col 91}{space 3} .5059447
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 2: Improve 
. svy: logit immigration_improve i.degree_dummy if infullmodel_TableD4 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   1{txt},{res}   3962{txt}){col 67}= {res}     18.92
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}  Linearized
{col 1}immigration_improve{col 21}{c |} Odds Ratio{col 33}   Std. Err.{col 45}      t{col 53}   P>|t|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}degree_dummy {c |}
{space 5}Degree-Holder  {c |}{col 21}{res}{space 2} 2.066797{col 33}{space 2}  .344991{col 44}{space 1}    4.35{col 53}{space 3}0.000{col 61}{space 4} 1.489949{col 74}{space 3} 2.866978
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}  .037531{col 33}{space 2} .0050195{col 44}{space 1}  -24.54{col 53}{space 3}0.000{col 61}{space 4} .0288744{col 74}{space 3} .0487828
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit immigration_improve i.degree_dummy $controls_demographics if infullmodel_TableD4 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      8.18
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                 immigration_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.582045{col 50}{space 2} .2810231{col 61}{space 1}    2.58{col 70}{space 3}0.010{col 78}{space 4} 1.116789{col 91}{space 3} 2.241126
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .5238864{col 50}{space 2} .1042154{col 61}{space 1}   -3.25{col 70}{space 3}0.001{col 78}{space 4} .3546975{col 91}{space 3} .7737776
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .2096789{col 50}{space 2}  .055851{col 61}{space 1}   -5.86{col 70}{space 3}0.000{col 78}{space 4} .1243815{col 91}{space 3} .3534709
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6261418{col 50}{space 2} .1042278{col 61}{space 1}   -2.81{col 70}{space 3}0.005{col 78}{space 4} .4517915{col 91}{space 3} .8677754
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .8706222{col 50}{space 2} .2078136{col 61}{space 1}   -0.58{col 70}{space 3}0.562{col 78}{space 4} .5452426{col 91}{space 3} 1.390176
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.493342{col 50}{space 2} .2813514{col 61}{space 1}    2.13{col 70}{space 3}0.033{col 78}{space 4} 1.032144{col 91}{space 3} 2.160617
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.580833{col 50}{space 2} .5289845{col 61}{space 1}    1.37{col 70}{space 3}0.171{col 78}{space 4} .8202885{col 91}{space 3}  3.04653
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 2.312422{col 50}{space 2} .6619176{col 61}{space 1}    2.93{col 70}{space 3}0.003{col 78}{space 4} 1.319292{col 91}{space 3} 4.053154
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2}  2.78285{col 50}{space 2} 1.382005{col 61}{space 1}    2.06{col 70}{space 3}0.039{col 78}{space 4} 1.051093{col 91}{space 3}  7.36781
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .3807654{col 50}{space 2} .1290625{col 61}{space 1}   -2.85{col 70}{space 3}0.004{col 78}{space 4}  .195907{col 91}{space 3} .7400566
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .3380461{col 50}{space 2} .0996209{col 61}{space 1}   -3.68{col 70}{space 3}0.000{col 78}{space 4} .1896939{col 91}{space 3} .6024189
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .2992312{col 50}{space 2} .0888934{col 61}{space 1}   -4.06{col 70}{space 3}0.000{col 78}{space 4} .1671325{col 91}{space 3} .5357385
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .3015157{col 50}{space 2} .0876506{col 61}{space 1}   -4.12{col 70}{space 3}0.000{col 78}{space 4} .1705257{col 91}{space 3}  .533126
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}  .677761{col 50}{space 2} .1575544{col 61}{space 1}   -1.67{col 70}{space 3}0.094{col 78}{space 4}  .429678{col 91}{space 3}  1.06908
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} .4170093{col 50}{space 2} .1227577{col 61}{space 1}   -2.97{col 70}{space 3}0.003{col 78}{space 4} .2341507{col 91}{space 3} .7426702
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .5801413{col 50}{space 2} .1648195{col 61}{space 1}   -1.92{col 70}{space 3}0.055{col 78}{space 4} .3323774{col 91}{space 3} 1.012596
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}  .192826{col 50}{space 2} .0788054{col 61}{space 1}   -4.03{col 70}{space 3}0.000{col 78}{space 4} .0865335{col 91}{space 3} .4296815
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .3295976{col 50}{space 2} .1196335{col 61}{space 1}   -3.06{col 70}{space 3}0.002{col 78}{space 4}  .161782{col 91}{space 3} .6714872
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit immigration_improve i.degree_dummy $controls_demographics $controls_objective if infullmodel_TableD4 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      6.65
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                 immigration_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  1.59962{col 50}{space 2} .2886746{col 61}{space 1}    2.60{col 70}{space 3}0.009{col 78}{space 4} 1.122945{col 91}{space 3} 2.278639
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .5450086{col 50}{space 2} .1092392{col 61}{space 1}   -3.03{col 70}{space 3}0.002{col 78}{space 4} .3679086{col 91}{space 3} .8073591
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .2160861{col 50}{space 2} .0575362{col 61}{space 1}   -5.75{col 70}{space 3}0.000{col 78}{space 4} .1282072{col 91}{space 3} .3642011
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6327077{col 50}{space 2} .1066214{col 61}{space 1}   -2.72{col 70}{space 3}0.007{col 78}{space 4} .4546929{col 91}{space 3} .8804165
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .8750304{col 50}{space 2} .2127652{col 61}{space 1}   -0.55{col 70}{space 3}0.583{col 78}{space 4} .5432362{col 91}{space 3} 1.409476
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.530704{col 50}{space 2} .2892899{col 61}{space 1}    2.25{col 70}{space 3}0.024{col 78}{space 4}  1.05675{col 91}{space 3} 2.217227
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}   1.5984{col 50}{space 2} .5418812{col 61}{space 1}    1.38{col 70}{space 3}0.167{col 78}{space 4} .8222948{col 91}{space 3} 3.107017
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 2.379369{col 50}{space 2} .6929929{col 61}{space 1}    2.98{col 70}{space 3}0.003{col 78}{space 4} 1.344228{col 91}{space 3} 4.211634
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 2.843035{col 50}{space 2} 1.420095{col 61}{space 1}    2.09{col 70}{space 3}0.037{col 78}{space 4} 1.067769{col 91}{space 3} 7.569848
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .3855199{col 50}{space 2} .1295655{col 61}{space 1}   -2.84{col 70}{space 3}0.005{col 78}{space 4} .1994746{col 91}{space 3} .7450853
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .3391026{col 50}{space 2}   .10098{col 61}{space 1}   -3.63{col 70}{space 3}0.000{col 78}{space 4} .1891376{col 91}{space 3} .6079734
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .2984927{col 50}{space 2} .0892649{col 61}{space 1}   -4.04{col 70}{space 3}0.000{col 78}{space 4} .1660742{col 91}{space 3} .5364944
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .2989838{col 50}{space 2} .0867787{col 61}{space 1}   -4.16{col 70}{space 3}0.000{col 78}{space 4} .1692446{col 91}{space 3} .5281783
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .6383978{col 50}{space 2}  .155782{col 61}{space 1}   -1.84{col 70}{space 3}0.066{col 78}{space 4} .3956557{col 91}{space 3} 1.030067
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} .5915131{col 50}{space 2} .2114711{col 61}{space 1}   -1.47{col 70}{space 3}0.142{col 78}{space 4} .2934671{col 91}{space 3} 1.192256
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .6668142{col 50}{space 2} .2494675{col 61}{space 1}   -1.08{col 70}{space 3}0.279{col 78}{space 4} .3202265{col 91}{space 3} 1.388521
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .2398477{col 50}{space 2} .1187657{col 61}{space 1}   -2.88{col 70}{space 3}0.004{col 78}{space 4} .0908481{col 91}{space 3} .6332204
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.153029{col 50}{space 2} .2352344{col 61}{space 1}    0.70{col 70}{space 3}0.485{col 78}{space 4}  .772912{col 91}{space 3} 1.720088
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} .9666115{col 50}{space 2} .4463419{col 61}{space 1}   -0.07{col 70}{space 3}0.941{col 78}{space 4} .3909143{col 91}{space 3} 2.390134
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} .9426562{col 50}{space 2} .4153522{col 61}{space 1}   -0.13{col 70}{space 3}0.893{col 78}{space 4} .3973591{col 91}{space 3} 2.236266
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .5014466{col 50}{space 2} .2101865{col 61}{space 1}   -1.65{col 70}{space 3}0.100{col 78}{space 4}  .220458{col 91}{space 3} 1.140574
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .6785262{col 50}{space 2} .1817789{col 61}{space 1}   -1.45{col 70}{space 3}0.148{col 78}{space 4} .4012896{col 91}{space 3} 1.147296
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.120495{col 50}{space 2} .2439613{col 61}{space 1}    0.52{col 70}{space 3}0.601{col 78}{space 4} .7311801{col 91}{space 3}   1.7171
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2961112{col 50}{space 2} .1385063{col 61}{space 1}   -2.60{col 70}{space 3}0.009{col 78}{space 4}  .118354{col 91}{space 3} .7408443
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 3: Worsen v Improve
. svy: logit immigration_directionworse i.degree_dummy if infullmodel_TableD4 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       907
{txt}{col 1}Number of PSUs{col 20}= {res}      907{txt}{col 49}Population size{col 67}={res} 1,006.7824
{txt}{col 49}Design df{col 67}= {res}       906
{txt}{col 49}F({res}   1{txt},{res}    906{txt}){col 67}= {res}     38.88
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}  Linearized
{col 1}immigration_directionworse{col 28}{c |} Odds Ratio{col 40}   Std. Err.{col 52}      t{col 60}   P>|t|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}degree_dummy {c |}
{space 12}Degree-Holder  {c |}{col 28}{res}{space 2} .3159416{col 40}{space 2} .0583815{col 51}{space 1}   -6.24{col 60}{space 3}0.000{col 68}{space 4} .2198399{col 81}{space 3} .4540536
{txt}{space 21}_cons {c |}{col 28}{res}{space 2} 6.444622{col 40}{space 2} .9119305{col 51}{space 1}   13.17{col 60}{space 3}0.000{col 68}{space 4} 4.881903{col 81}{space 3} 8.507575
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit immigration_directionworse i.degree_dummy $controls_demographics if infullmodel_TableD4 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       907
{txt}{col 1}Number of PSUs{col 20}= {res}      907{txt}{col 49}Population size{col 67}={res} 1,006.7824
{txt}{col 49}Design df{col 67}= {res}       906
{txt}{col 49}F({res}  17{txt},{res}    890{txt}){col 67}= {res}      8.38
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}          immigration_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .5189287{col 50}{space 2} .1065262{col 61}{space 1}   -3.20{col 70}{space 3}0.001{col 78}{space 4} .3468474{col 91}{space 3} .7763846
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 2.527205{col 50}{space 2} .6544328{col 61}{space 1}    3.58{col 70}{space 3}0.000{col 78}{space 4} 1.520277{col 91}{space 3} 4.201055
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 7.166644{col 50}{space 2} 2.219853{col 61}{space 1}    6.36{col 70}{space 3}0.000{col 78}{space 4} 3.902162{col 91}{space 3} 13.16214
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .8566872{col 50}{space 2} .1800572{col 61}{space 1}   -0.74{col 70}{space 3}0.462{col 78}{space 4} .5671249{col 91}{space 3} 1.294094
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.018112{col 50}{space 2} .2694679{col 61}{space 1}    0.07{col 70}{space 3}0.946{col 78}{space 4}  .605624{col 91}{space 3} 1.711543
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .6242586{col 50}{space 2}   .13483{col 61}{space 1}   -2.18{col 70}{space 3}0.029{col 78}{space 4} .4085756{col 91}{space 3} .9537984
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}  .373052{col 50}{space 2} .1877147{col 61}{space 1}   -1.96{col 70}{space 3}0.050{col 78}{space 4}  .138958{col 91}{space 3}  1.00151
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .3478355{col 50}{space 2} .1153555{col 61}{space 1}   -3.18{col 70}{space 3}0.002{col 78}{space 4} .1814284{col 91}{space 3}  .666872
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .1620861{col 50}{space 2} .1142552{col 61}{space 1}   -2.58{col 70}{space 3}0.010{col 78}{space 4} .0406376{col 91}{space 3} .6464936
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.480791{col 50}{space 2} .5637549{col 61}{space 1}    1.03{col 70}{space 3}0.303{col 78}{space 4} .7014516{col 91}{space 3} 3.126007
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.469768{col 50}{space 2} .5058497{col 61}{space 1}    1.12{col 70}{space 3}0.263{col 78}{space 4} .7479959{col 91}{space 3} 2.888009
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.895567{col 50}{space 2} .6429994{col 61}{space 1}    1.89{col 70}{space 3}0.060{col 78}{space 4} .9741258{col 91}{space 3} 3.688615
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.503835{col 50}{space 2}  .508504{col 61}{space 1}    1.21{col 70}{space 3}0.228{col 78}{space 4} .7744465{col 91}{space 3} 2.920175
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.231503{col 50}{space 2} .3397895{col 61}{space 1}    0.75{col 70}{space 3}0.451{col 78}{space 4} .7165767{col 91}{space 3} 2.116453
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}  3.19873{col 50}{space 2} 1.102565{col 61}{space 1}    3.37{col 70}{space 3}0.001{col 78}{space 4} 1.626245{col 91}{space 3}  6.29172
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.799199{col 50}{space 2} .6013717{col 61}{space 1}    1.76{col 70}{space 3}0.079{col 78}{space 4} .9336613{col 91}{space 3}  3.46712
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}  7.46925{col 50}{space 2} 2.924152{col 61}{space 1}    5.14{col 70}{space 3}0.000{col 78}{space 4} 3.464117{col 91}{space 3} 16.10503
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 1.189334{col 50}{space 2} .4885172{col 61}{space 1}    0.42{col 70}{space 3}0.673{col 78}{space 4} .5311367{col 91}{space 3} 2.663185
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit immigration_directionworse i.degree_dummy $controls_demographics $controls_objective if infullmodel_TableD4 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       907
{txt}{col 1}Number of PSUs{col 20}= {res}      907{txt}{col 49}Population size{col 67}={res} 1,006.7824
{txt}{col 49}Design df{col 67}= {res}       906
{txt}{col 49}F({res}  23{txt},{res}    884{txt}){col 67}= {res}      6.72
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}          immigration_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  .516291{col 50}{space 2} .1088358{col 61}{space 1}   -3.14{col 70}{space 3}0.002{col 78}{space 4} .3413647{col 91}{space 3} .7808553
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 2.559466{col 50}{space 2} .6820355{col 61}{space 1}    3.53{col 70}{space 3}0.000{col 78}{space 4} 1.517125{col 91}{space 3} 4.317947
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 7.746819{col 50}{space 2} 2.422385{col 61}{space 1}    6.55{col 70}{space 3}0.000{col 78}{space 4} 4.193741{col 91}{space 3} 14.31018
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .8649187{col 50}{space 2} .1869625{col 61}{space 1}   -0.67{col 70}{space 3}0.502{col 78}{space 4} .5658897{col 91}{space 3} 1.321962
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.032428{col 50}{space 2} .2875799{col 61}{space 1}    0.11{col 70}{space 3}0.909{col 78}{space 4} .5976447{col 91}{space 3} 1.783515
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .5824175{col 50}{space 2}  .127716{col 61}{space 1}   -2.47{col 70}{space 3}0.014{col 78}{space 4} .3787287{col 91}{space 3} .8956549
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .3381561{col 50}{space 2}  .175906{col 61}{space 1}   -2.08{col 70}{space 3}0.037{col 78}{space 4} .1218251{col 91}{space 3} .9386368
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .3337968{col 50}{space 2} .1166511{col 61}{space 1}   -3.14{col 70}{space 3}0.002{col 78}{space 4} .1681191{col 91}{space 3} .6627462
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .1477794{col 50}{space 2} .1025179{col 61}{space 1}   -2.76{col 70}{space 3}0.006{col 78}{space 4} .0378727{col 91}{space 3} .5766358
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}  1.51432{col 50}{space 2} .5844268{col 61}{space 1}    1.08{col 70}{space 3}0.283{col 78}{space 4} .7100206{col 91}{space 3} 3.229716
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.333983{col 50}{space 2}  .476722{col 61}{space 1}    0.81{col 70}{space 3}0.420{col 78}{space 4}  .661533{col 91}{space 3} 2.689979
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.908907{col 50}{space 2}  .665307{col 61}{space 1}    1.86{col 70}{space 3}0.064{col 78}{space 4} .9632094{col 91}{space 3}  3.78311
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.494628{col 50}{space 2} .5094426{col 61}{space 1}    1.18{col 70}{space 3}0.239{col 78}{space 4} .7656208{col 91}{space 3}  2.91778
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.441223{col 50}{space 2} .4333198{col 61}{space 1}    1.22{col 70}{space 3}0.224{col 78}{space 4} .7988503{col 91}{space 3}  2.60014
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 2.484735{col 50}{space 2} 1.243821{col 61}{space 1}    1.82{col 70}{space 3}0.069{col 78}{space 4}  .930275{col 91}{space 3} 6.636646
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.433121{col 50}{space 2} .5973314{col 61}{space 1}    0.86{col 70}{space 3}0.388{col 78}{space 4} .6324459{col 91}{space 3} 3.247448
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 5.020165{col 50}{space 2} 2.570767{col 61}{space 1}    3.15{col 70}{space 3}0.002{col 78}{space 4} 1.837574{col 91}{space 3} 13.71486
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .6914669{col 50}{space 2} .1766033{col 61}{space 1}   -1.44{col 70}{space 3}0.149{col 78}{space 4} .4188712{col 91}{space 3} 1.141464
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.845413{col 50}{space 2} .9589806{col 61}{space 1}    1.18{col 70}{space 3}0.239{col 78}{space 4} .6655332{col 91}{space 3} 5.117026
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.537274{col 50}{space 2} .7708171{col 61}{space 1}    0.86{col 70}{space 3}0.391{col 78}{space 4}  .574609{col 91}{space 3} 4.112729
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2}  1.39818{col 50}{space 2} .8245047{col 61}{space 1}    0.57{col 70}{space 3}0.570{col 78}{space 4} .4394799{col 91}{space 3} 4.448226
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.470139{col 50}{space 2} .4711989{col 61}{space 1}    1.20{col 70}{space 3}0.230{col 78}{space 4} .7837406{col 91}{space 3} 2.757685
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} .5774212{col 50}{space 2} .1611051{col 61}{space 1}   -1.97{col 70}{space 3}0.049{col 78}{space 4} .3339512{col 91}{space 3} .9983952
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 1.180449{col 50}{space 2} .6219186{col 61}{space 1}    0.31{col 70}{space 3}0.753{col 78}{space 4}  .419752{col 91}{space 3} 3.319724
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. 
. ******* ISCO-08 Occupational Group ********
. 
. * Model 1: Worsen 
. svy: logit immigration_worsen i.isco08_5group if infullmodel_TableD4 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   4{txt},{res}   3959{txt}){col 67}= {res}     14.05
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                immigration_worsen{col 36}{c |} Odds Ratio{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}isco08_5group {c |}
Technician/Associate Professional  {c |}{col 36}{res}{space 2} .9448382{col 48}{space 2} .1313884{col 59}{space 1}   -0.41{col 68}{space 3}0.683{col 76}{space 4} .7193726{col 89}{space 3} 1.240969
{txt}{space 9}Clerical Support Workers  {c |}{col 36}{res}{space 2} 1.412573{col 48}{space 2} .1872928{col 59}{space 1}    2.61{col 68}{space 3}0.009{col 76}{space 4}  1.08922{col 89}{space 3} 1.831918
{txt}{space 8}Service and Sales Workers  {c |}{col 36}{res}{space 2} 1.162075{col 48}{space 2} .1654063{col 59}{space 1}    1.06{col 68}{space 3}0.291{col 76}{space 4} .8791028{col 89}{space 3} 1.536132
{txt}{space 13}Manual Working Class  {c |}{col 36}{res}{space 2} 2.550007{col 48}{space 2} .3472067{col 59}{space 1}    6.88{col 68}{space 3}0.000{col 76}{space 4} 1.952572{col 89}{space 3} 3.330241
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .1904359{col 48}{space 2} .0147095{col 59}{space 1}  -21.47{col 68}{space 3}0.000{col 76}{space 4} .1636744{col 89}{space 3} .2215731
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit immigration_worsen i.isco08_5group $controls_demographics if infullmodel_TableD4 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      7.86
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                  immigration_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .8561006{col 50}{space 2} .1220303{col 61}{space 1}   -1.09{col 70}{space 3}0.276{col 78}{space 4} .6473746{col 91}{space 3} 1.132124
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.278422{col 50}{space 2} .1836731{col 61}{space 1}    1.71{col 70}{space 3}0.087{col 78}{space 4} .9645912{col 91}{space 3} 1.694358
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.037038{col 50}{space 2} .1644867{col 61}{space 1}    0.23{col 70}{space 3}0.819{col 78}{space 4} .7598757{col 91}{space 3} 1.415294
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}   1.7023{col 50}{space 2} .2648454{col 61}{space 1}    3.42{col 70}{space 3}0.001{col 78}{space 4} 1.254772{col 91}{space 3} 2.309443
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.492692{col 50}{space 2} .2349607{col 61}{space 1}    2.54{col 70}{space 3}0.011{col 78}{space 4} 1.096336{col 91}{space 3}  2.03234
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.610056{col 50}{space 2} .2627392{col 61}{space 1}    2.92{col 70}{space 3}0.004{col 78}{space 4} 1.169214{col 91}{space 3} 2.217113
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .5958004{col 50}{space 2} .0580761{col 61}{space 1}   -5.31{col 70}{space 3}0.000{col 78}{space 4} .4921573{col 91}{space 3} .7212696
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7248211{col 50}{space 2}  .076892{col 61}{space 1}   -3.03{col 70}{space 3}0.002{col 78}{space 4} .5887139{col 91}{space 3} .8923955
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.042172{col 50}{space 2} .1260904{col 61}{space 1}    0.34{col 70}{space 3}0.733{col 78}{space 4} .8220966{col 91}{space 3} 1.321163
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .8324747{col 50}{space 2} .0880657{col 61}{space 1}   -1.73{col 70}{space 3}0.083{col 78}{space 4} .6765451{col 91}{space 3} 1.024343
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .5974396{col 50}{space 2} .1563604{col 61}{space 1}   -1.97{col 70}{space 3}0.049{col 78}{space 4} .3576446{col 91}{space 3} .9980136
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .7180797{col 50}{space 2}  .123263{col 61}{space 1}   -1.93{col 70}{space 3}0.054{col 78}{space 4} .5128777{col 91}{space 3} 1.005383
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5878524{col 50}{space 2} .1865065{col 61}{space 1}   -1.67{col 70}{space 3}0.094{col 78}{space 4} .3155928{col 91}{space 3} 1.094988
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .6709222{col 50}{space 2} .1149382{col 61}{space 1}   -2.33{col 70}{space 3}0.020{col 78}{space 4} .4795182{col 91}{space 3} .9387268
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .7103922{col 50}{space 2} .1180463{col 61}{space 1}   -2.06{col 70}{space 3}0.040{col 78}{space 4} .5128735{col 91}{space 3} .9839796
{txt}{space 34}4  {c |}{col 38}{res}{space 2}  .780437{col 50}{space 2} .1288428{col 61}{space 1}   -1.50{col 70}{space 3}0.133{col 78}{space 4} .5646373{col 91}{space 3} 1.078713
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .5720486{col 50}{space 2}  .100061{col 61}{space 1}   -3.19{col 70}{space 3}0.001{col 78}{space 4} .4059736{col 91}{space 3} .8060612
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .3355516{col 50}{space 2} .0770668{col 61}{space 1}   -4.75{col 70}{space 3}0.000{col 78}{space 4} .2138958{col 91}{space 3} .5264005
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit immigration_worsen i.isco08_5group $controls_demographics $controls_objective if infullmodel_TableD4 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      6.22
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                  immigration_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .8419726{col 50}{space 2} .1299202{col 61}{space 1}   -1.11{col 70}{space 3}0.265{col 78}{space 4} .6221765{col 91}{space 3} 1.139416
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.219233{col 50}{space 2} .2865309{col 61}{space 1}    0.84{col 70}{space 3}0.399{col 78}{space 4}  .769105{col 91}{space 3} 1.932805
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .8823133{col 50}{space 2} .1578064{col 61}{space 1}   -0.70{col 70}{space 3}0.484{col 78}{space 4} .6213473{col 91}{space 3} 1.252885
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.365186{col 50}{space 2} .2966848{col 61}{space 1}    1.43{col 70}{space 3}0.152{col 78}{space 4} .8915596{col 91}{space 3} 2.090417
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.494482{col 50}{space 2} .2352497{col 61}{space 1}    2.55{col 70}{space 3}0.011{col 78}{space 4} 1.097641{col 91}{space 3} 2.034797
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.610733{col 50}{space 2} .2628841{col 61}{space 1}    2.92{col 70}{space 3}0.004{col 78}{space 4} 1.169657{col 91}{space 3} 2.218139
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}  .599543{col 50}{space 2} .0587938{col 61}{space 1}   -5.22{col 70}{space 3}0.000{col 78}{space 4} .4946776{col 91}{space 3} .7266385
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7254971{col 50}{space 2} .0774474{col 61}{space 1}   -3.01{col 70}{space 3}0.003{col 78}{space 4} .5884933{col 91}{space 3} .8943961
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.018511{col 50}{space 2} .1252356{col 61}{space 1}    0.15{col 70}{space 3}0.881{col 78}{space 4} .8003321{col 91}{space 3} 1.296167
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2}  .822743{col 50}{space 2} .0896053{col 61}{space 1}   -1.79{col 70}{space 3}0.073{col 78}{space 4} .6645552{col 91}{space 3} 1.018585
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .6033868{col 50}{space 2} .1581631{col 61}{space 1}   -1.93{col 70}{space 3}0.054{col 78}{space 4} .3609159{col 91}{space 3} 1.008755
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .7067927{col 50}{space 2} .1216358{col 61}{space 1}   -2.02{col 70}{space 3}0.044{col 78}{space 4} .5043818{col 91}{space 3} .9904322
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5794473{col 50}{space 2} .1826008{col 61}{space 1}   -1.73{col 70}{space 3}0.083{col 78}{space 4} .3123873{col 91}{space 3} 1.074817
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .6693483{col 50}{space 2} .1154689{col 61}{space 1}   -2.33{col 70}{space 3}0.020{col 78}{space 4} .4772732{col 91}{space 3} .9387227
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .7129928{col 50}{space 2} .1187527{col 61}{space 1}   -2.03{col 70}{space 3}0.042{col 78}{space 4} .5143631{col 91}{space 3} .9883268
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .7970378{col 50}{space 2} .1319035{col 61}{space 1}   -1.37{col 70}{space 3}0.171{col 78}{space 4}  .576194{col 91}{space 3} 1.102527
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .5898411{col 50}{space 2} .1036875{col 61}{space 1}   -3.00{col 70}{space 3}0.003{col 78}{space 4} .4178858{col 91}{space 3} .8325541
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .7326398{col 50}{space 2}   .08772{col 61}{space 1}   -2.60{col 70}{space 3}0.009{col 78}{space 4} .5793538{col 91}{space 3} .9264825
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.905536{col 50}{space 2} .4550764{col 61}{space 1}    2.70{col 70}{space 3}0.007{col 78}{space 4} 1.193089{col 91}{space 3} 3.043418
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2}  1.46295{col 50}{space 2} .3511115{col 61}{space 1}    1.59{col 70}{space 3}0.113{col 78}{space 4} .9138537{col 91}{space 3} 2.341975
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .8878147{col 50}{space 2} .2416446{col 61}{space 1}   -0.44{col 70}{space 3}0.662{col 78}{space 4} .5206817{col 91}{space 3} 1.513814
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.083534{col 50}{space 2} .1647442{col 61}{space 1}    0.53{col 70}{space 3}0.598{col 78}{space 4} .8042362{col 91}{space 3} 1.459828
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} .7759497{col 50}{space 2} .1117268{col 61}{space 1}   -1.76{col 70}{space 3}0.078{col 78}{space 4} .5851052{col 91}{space 3} 1.029042
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2}  .295907{col 50}{space 2} .0809559{col 61}{space 1}   -4.45{col 70}{space 3}0.000{col 78}{space 4} .1730643{col 91}{space 3} .5059447
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 2: Improve 
. svy: logit immigration_improve i.isco08_5group if infullmodel_TableD4 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   4{txt},{res}   3959{txt}){col 67}= {res}      6.71
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}               immigration_improve{col 36}{c |} Odds Ratio{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}isco08_5group {c |}
Technician/Associate Professional  {c |}{col 36}{res}{space 2} .6485866{col 48}{space 2} .1446969{col 59}{space 1}   -1.94{col 68}{space 3}0.052{col 76}{space 4} .4188037{col 89}{space 3} 1.004443
{txt}{space 9}Clerical Support Workers  {c |}{col 36}{res}{space 2} .3722009{col 48}{space 2} .0982453{col 59}{space 1}   -3.74{col 68}{space 3}0.000{col 76}{space 4} .2218337{col 89}{space 3} .6244926
{txt}{space 8}Service and Sales Workers  {c |}{col 36}{res}{space 2} .6215758{col 48}{space 2} .1635881{col 59}{space 1}   -1.81{col 68}{space 3}0.071{col 76}{space 4} .3710257{col 89}{space 3}  1.04132
{txt}{space 13}Manual Working Class  {c |}{col 36}{res}{space 2} .2123146{col 48}{space 2} .0888978{col 59}{space 1}   -3.70{col 68}{space 3}0.000{col 76}{space 4} .0934257{col 89}{space 3} .4824957
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .0832008{col 48}{space 2} .0086264{col 59}{space 1}  -23.98{col 68}{space 3}0.000{col 76}{space 4} .0678963{col 89}{space 3} .1019551
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit immigration_improve i.isco08_5group $controls_demographics if infullmodel_TableD4 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      8.18
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                 immigration_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}  .677761{col 50}{space 2} .1575544{col 61}{space 1}   -1.67{col 70}{space 3}0.094{col 78}{space 4}  .429678{col 91}{space 3}  1.06908
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} .4170093{col 50}{space 2} .1227577{col 61}{space 1}   -2.97{col 70}{space 3}0.003{col 78}{space 4} .2341507{col 91}{space 3} .7426702
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .5801413{col 50}{space 2} .1648195{col 61}{space 1}   -1.92{col 70}{space 3}0.055{col 78}{space 4} .3323774{col 91}{space 3} 1.012596
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}  .192826{col 50}{space 2} .0788054{col 61}{space 1}   -4.03{col 70}{space 3}0.000{col 78}{space 4} .0865335{col 91}{space 3} .4296815
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .5238864{col 50}{space 2} .1042154{col 61}{space 1}   -3.25{col 70}{space 3}0.001{col 78}{space 4} .3546975{col 91}{space 3} .7737776
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .2096789{col 50}{space 2}  .055851{col 61}{space 1}   -5.86{col 70}{space 3}0.000{col 78}{space 4} .1243815{col 91}{space 3} .3534709
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6261418{col 50}{space 2} .1042278{col 61}{space 1}   -2.81{col 70}{space 3}0.005{col 78}{space 4} .4517915{col 91}{space 3} .8677754
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.582045{col 50}{space 2} .2810231{col 61}{space 1}    2.58{col 70}{space 3}0.010{col 78}{space 4} 1.116789{col 91}{space 3} 2.241126
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .8706222{col 50}{space 2} .2078136{col 61}{space 1}   -0.58{col 70}{space 3}0.562{col 78}{space 4} .5452426{col 91}{space 3} 1.390176
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.493342{col 50}{space 2} .2813514{col 61}{space 1}    2.13{col 70}{space 3}0.033{col 78}{space 4} 1.032144{col 91}{space 3} 2.160617
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.580833{col 50}{space 2} .5289845{col 61}{space 1}    1.37{col 70}{space 3}0.171{col 78}{space 4} .8202885{col 91}{space 3}  3.04653
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 2.312422{col 50}{space 2} .6619176{col 61}{space 1}    2.93{col 70}{space 3}0.003{col 78}{space 4} 1.319292{col 91}{space 3} 4.053154
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2}  2.78285{col 50}{space 2} 1.382005{col 61}{space 1}    2.06{col 70}{space 3}0.039{col 78}{space 4} 1.051093{col 91}{space 3}  7.36781
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .3807654{col 50}{space 2} .1290625{col 61}{space 1}   -2.85{col 70}{space 3}0.004{col 78}{space 4}  .195907{col 91}{space 3} .7400566
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .3380461{col 50}{space 2} .0996209{col 61}{space 1}   -3.68{col 70}{space 3}0.000{col 78}{space 4} .1896939{col 91}{space 3} .6024189
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .2992312{col 50}{space 2} .0888934{col 61}{space 1}   -4.06{col 70}{space 3}0.000{col 78}{space 4} .1671325{col 91}{space 3} .5357385
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .3015157{col 50}{space 2} .0876506{col 61}{space 1}   -4.12{col 70}{space 3}0.000{col 78}{space 4} .1705257{col 91}{space 3}  .533126
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .3295976{col 50}{space 2} .1196335{col 61}{space 1}   -3.06{col 70}{space 3}0.002{col 78}{space 4}  .161782{col 91}{space 3} .6714872
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit immigration_improve i.isco08_5group $controls_demographics $controls_objective if infullmodel_TableD4 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      6.65
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                 immigration_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .6383978{col 50}{space 2}  .155782{col 61}{space 1}   -1.84{col 70}{space 3}0.066{col 78}{space 4} .3956557{col 91}{space 3} 1.030067
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} .5915131{col 50}{space 2} .2114711{col 61}{space 1}   -1.47{col 70}{space 3}0.142{col 78}{space 4} .2934671{col 91}{space 3} 1.192256
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .6668142{col 50}{space 2} .2494675{col 61}{space 1}   -1.08{col 70}{space 3}0.279{col 78}{space 4} .3202265{col 91}{space 3} 1.388521
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .2398477{col 50}{space 2} .1187657{col 61}{space 1}   -2.88{col 70}{space 3}0.004{col 78}{space 4} .0908481{col 91}{space 3} .6332204
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .5450086{col 50}{space 2} .1092392{col 61}{space 1}   -3.03{col 70}{space 3}0.002{col 78}{space 4} .3679086{col 91}{space 3} .8073591
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .2160861{col 50}{space 2} .0575362{col 61}{space 1}   -5.75{col 70}{space 3}0.000{col 78}{space 4} .1282072{col 91}{space 3} .3642011
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6327077{col 50}{space 2} .1066214{col 61}{space 1}   -2.72{col 70}{space 3}0.007{col 78}{space 4} .4546929{col 91}{space 3} .8804165
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  1.59962{col 50}{space 2} .2886746{col 61}{space 1}    2.60{col 70}{space 3}0.009{col 78}{space 4} 1.122945{col 91}{space 3} 2.278639
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .8750304{col 50}{space 2} .2127652{col 61}{space 1}   -0.55{col 70}{space 3}0.583{col 78}{space 4} .5432362{col 91}{space 3} 1.409476
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.530704{col 50}{space 2} .2892899{col 61}{space 1}    2.25{col 70}{space 3}0.024{col 78}{space 4}  1.05675{col 91}{space 3} 2.217227
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}   1.5984{col 50}{space 2} .5418812{col 61}{space 1}    1.38{col 70}{space 3}0.167{col 78}{space 4} .8222948{col 91}{space 3} 3.107017
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 2.379369{col 50}{space 2} .6929929{col 61}{space 1}    2.98{col 70}{space 3}0.003{col 78}{space 4} 1.344228{col 91}{space 3} 4.211634
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 2.843035{col 50}{space 2} 1.420095{col 61}{space 1}    2.09{col 70}{space 3}0.037{col 78}{space 4} 1.067769{col 91}{space 3} 7.569848
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .3855199{col 50}{space 2} .1295655{col 61}{space 1}   -2.84{col 70}{space 3}0.005{col 78}{space 4} .1994746{col 91}{space 3} .7450853
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .3391026{col 50}{space 2}   .10098{col 61}{space 1}   -3.63{col 70}{space 3}0.000{col 78}{space 4} .1891376{col 91}{space 3} .6079734
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .2984927{col 50}{space 2} .0892649{col 61}{space 1}   -4.04{col 70}{space 3}0.000{col 78}{space 4} .1660742{col 91}{space 3} .5364944
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .2989838{col 50}{space 2} .0867787{col 61}{space 1}   -4.16{col 70}{space 3}0.000{col 78}{space 4} .1692446{col 91}{space 3} .5281783
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.153029{col 50}{space 2} .2352344{col 61}{space 1}    0.70{col 70}{space 3}0.485{col 78}{space 4}  .772912{col 91}{space 3} 1.720088
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} .9666115{col 50}{space 2} .4463419{col 61}{space 1}   -0.07{col 70}{space 3}0.941{col 78}{space 4} .3909143{col 91}{space 3} 2.390134
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} .9426562{col 50}{space 2} .4153522{col 61}{space 1}   -0.13{col 70}{space 3}0.893{col 78}{space 4} .3973591{col 91}{space 3} 2.236266
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .5014466{col 50}{space 2} .2101865{col 61}{space 1}   -1.65{col 70}{space 3}0.100{col 78}{space 4}  .220458{col 91}{space 3} 1.140574
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .6785262{col 50}{space 2} .1817789{col 61}{space 1}   -1.45{col 70}{space 3}0.148{col 78}{space 4} .4012896{col 91}{space 3} 1.147296
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.120495{col 50}{space 2} .2439613{col 61}{space 1}    0.52{col 70}{space 3}0.601{col 78}{space 4} .7311801{col 91}{space 3}   1.7171
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2961112{col 50}{space 2} .1385063{col 61}{space 1}   -2.60{col 70}{space 3}0.009{col 78}{space 4}  .118354{col 91}{space 3} .7408443
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 3: Worsen v Improve
. svy: logit immigration_directionworse i.isco08_5group if infullmodel_TableD4 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       907
{txt}{col 1}Number of PSUs{col 20}= {res}      907{txt}{col 49}Population size{col 67}={res} 1,006.7824
{txt}{col 49}Design df{col 67}= {res}       906
{txt}{col 49}F({res}   4{txt},{res}    903{txt}){col 67}= {res}     10.01
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}        immigration_directionworse{col 36}{c |} Odds Ratio{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}isco08_5group {c |}
Technician/Associate Professional  {c |}{col 36}{res}{space 2} 1.430063{col 48}{space 2} .3607524{col 59}{space 1}    1.42{col 68}{space 3}0.157{col 76}{space 4} .8716478{col 89}{space 3} 2.346223
{txt}{space 9}Clerical Support Workers  {c |}{col 36}{res}{space 2} 3.388537{col 48}{space 2} .9677487{col 59}{space 1}    4.27{col 68}{space 3}0.000{col 76}{space 4} 1.934587{col 89}{space 3} 5.935212
{txt}{space 8}Service and Sales Workers  {c |}{col 36}{res}{space 2} 1.769346{col 48}{space 2} .5110455{col 59}{space 1}    1.98{col 68}{space 3}0.049{col 76}{space 4} 1.003757{col 89}{space 3} 3.118866
{txt}{space 13}Manual Working Class  {c |}{col 36}{res}{space 2} 9.041852{col 48}{space 2} 3.900215{col 59}{space 1}    5.10{col 68}{space 3}0.000{col 76}{space 4} 3.877924{col 89}{space 3} 21.08218
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} 2.082688{col 48}{space 2} .2554699{col 59}{space 1}    5.98{col 68}{space 3}0.000{col 76}{space 4} 1.637092{col 89}{space 3} 2.649569
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit immigration_directionworse i.isco08_5group $controls_demographics if infullmodel_TableD4 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       907
{txt}{col 1}Number of PSUs{col 20}= {res}      907{txt}{col 49}Population size{col 67}={res} 1,006.7824
{txt}{col 49}Design df{col 67}= {res}       906
{txt}{col 49}F({res}  17{txt},{res}    890{txt}){col 67}= {res}      8.38
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}          immigration_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.231503{col 50}{space 2} .3397895{col 61}{space 1}    0.75{col 70}{space 3}0.451{col 78}{space 4} .7165767{col 91}{space 3} 2.116453
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}  3.19873{col 50}{space 2} 1.102565{col 61}{space 1}    3.37{col 70}{space 3}0.001{col 78}{space 4} 1.626245{col 91}{space 3}  6.29172
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.799199{col 50}{space 2} .6013717{col 61}{space 1}    1.76{col 70}{space 3}0.079{col 78}{space 4} .9336613{col 91}{space 3}  3.46712
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}  7.46925{col 50}{space 2} 2.924152{col 61}{space 1}    5.14{col 70}{space 3}0.000{col 78}{space 4} 3.464117{col 91}{space 3} 16.10503
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 2.527205{col 50}{space 2} .6544328{col 61}{space 1}    3.58{col 70}{space 3}0.000{col 78}{space 4} 1.520277{col 91}{space 3} 4.201055
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 7.166644{col 50}{space 2} 2.219853{col 61}{space 1}    6.36{col 70}{space 3}0.000{col 78}{space 4} 3.902162{col 91}{space 3} 13.16214
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .8566872{col 50}{space 2} .1800572{col 61}{space 1}   -0.74{col 70}{space 3}0.462{col 78}{space 4} .5671249{col 91}{space 3} 1.294094
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .5189287{col 50}{space 2} .1065262{col 61}{space 1}   -3.20{col 70}{space 3}0.001{col 78}{space 4} .3468474{col 91}{space 3} .7763846
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.018112{col 50}{space 2} .2694679{col 61}{space 1}    0.07{col 70}{space 3}0.946{col 78}{space 4}  .605624{col 91}{space 3} 1.711543
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .6242586{col 50}{space 2}   .13483{col 61}{space 1}   -2.18{col 70}{space 3}0.029{col 78}{space 4} .4085756{col 91}{space 3} .9537984
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}  .373052{col 50}{space 2} .1877147{col 61}{space 1}   -1.96{col 70}{space 3}0.050{col 78}{space 4}  .138958{col 91}{space 3}  1.00151
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .3478355{col 50}{space 2} .1153555{col 61}{space 1}   -3.18{col 70}{space 3}0.002{col 78}{space 4} .1814284{col 91}{space 3}  .666872
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .1620861{col 50}{space 2} .1142552{col 61}{space 1}   -2.58{col 70}{space 3}0.010{col 78}{space 4} .0406376{col 91}{space 3} .6464936
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.480791{col 50}{space 2} .5637549{col 61}{space 1}    1.03{col 70}{space 3}0.303{col 78}{space 4} .7014516{col 91}{space 3} 3.126007
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.469768{col 50}{space 2} .5058497{col 61}{space 1}    1.12{col 70}{space 3}0.263{col 78}{space 4} .7479959{col 91}{space 3} 2.888009
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.895567{col 50}{space 2} .6429994{col 61}{space 1}    1.89{col 70}{space 3}0.060{col 78}{space 4} .9741258{col 91}{space 3} 3.688615
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.503835{col 50}{space 2}  .508504{col 61}{space 1}    1.21{col 70}{space 3}0.228{col 78}{space 4} .7744465{col 91}{space 3} 2.920175
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 1.189334{col 50}{space 2} .4885172{col 61}{space 1}    0.42{col 70}{space 3}0.673{col 78}{space 4} .5311367{col 91}{space 3} 2.663185
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit immigration_directionworse i.isco08_5group $controls_demographics $controls_objective if infullmodel_TableD4 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       907
{txt}{col 1}Number of PSUs{col 20}= {res}      907{txt}{col 49}Population size{col 67}={res} 1,006.7824
{txt}{col 49}Design df{col 67}= {res}       906
{txt}{col 49}F({res}  23{txt},{res}    884{txt}){col 67}= {res}      6.72
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}          immigration_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.441223{col 50}{space 2} .4333198{col 61}{space 1}    1.22{col 70}{space 3}0.224{col 78}{space 4} .7988503{col 91}{space 3}  2.60014
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 2.484735{col 50}{space 2} 1.243821{col 61}{space 1}    1.82{col 70}{space 3}0.069{col 78}{space 4}  .930275{col 91}{space 3} 6.636646
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.433121{col 50}{space 2} .5973314{col 61}{space 1}    0.86{col 70}{space 3}0.388{col 78}{space 4} .6324459{col 91}{space 3} 3.247448
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 5.020165{col 50}{space 2} 2.570767{col 61}{space 1}    3.15{col 70}{space 3}0.002{col 78}{space 4} 1.837574{col 91}{space 3} 13.71486
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 2.559466{col 50}{space 2} .6820355{col 61}{space 1}    3.53{col 70}{space 3}0.000{col 78}{space 4} 1.517125{col 91}{space 3} 4.317947
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 7.746819{col 50}{space 2} 2.422385{col 61}{space 1}    6.55{col 70}{space 3}0.000{col 78}{space 4} 4.193741{col 91}{space 3} 14.31018
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .8649187{col 50}{space 2} .1869625{col 61}{space 1}   -0.67{col 70}{space 3}0.502{col 78}{space 4} .5658897{col 91}{space 3} 1.321962
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  .516291{col 50}{space 2} .1088358{col 61}{space 1}   -3.14{col 70}{space 3}0.002{col 78}{space 4} .3413647{col 91}{space 3} .7808553
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.032428{col 50}{space 2} .2875799{col 61}{space 1}    0.11{col 70}{space 3}0.909{col 78}{space 4} .5976447{col 91}{space 3} 1.783515
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .5824175{col 50}{space 2}  .127716{col 61}{space 1}   -2.47{col 70}{space 3}0.014{col 78}{space 4} .3787287{col 91}{space 3} .8956549
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .3381561{col 50}{space 2}  .175906{col 61}{space 1}   -2.08{col 70}{space 3}0.037{col 78}{space 4} .1218251{col 91}{space 3} .9386368
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .3337968{col 50}{space 2} .1166511{col 61}{space 1}   -3.14{col 70}{space 3}0.002{col 78}{space 4} .1681191{col 91}{space 3} .6627462
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .1477794{col 50}{space 2} .1025179{col 61}{space 1}   -2.76{col 70}{space 3}0.006{col 78}{space 4} .0378727{col 91}{space 3} .5766358
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}  1.51432{col 50}{space 2} .5844268{col 61}{space 1}    1.08{col 70}{space 3}0.283{col 78}{space 4} .7100206{col 91}{space 3} 3.229716
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.333983{col 50}{space 2}  .476722{col 61}{space 1}    0.81{col 70}{space 3}0.420{col 78}{space 4}  .661533{col 91}{space 3} 2.689979
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.908907{col 50}{space 2}  .665307{col 61}{space 1}    1.86{col 70}{space 3}0.064{col 78}{space 4} .9632094{col 91}{space 3}  3.78311
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.494628{col 50}{space 2} .5094426{col 61}{space 1}    1.18{col 70}{space 3}0.239{col 78}{space 4} .7656208{col 91}{space 3}  2.91778
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .6914669{col 50}{space 2} .1766033{col 61}{space 1}   -1.44{col 70}{space 3}0.149{col 78}{space 4} .4188712{col 91}{space 3} 1.141464
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.845413{col 50}{space 2} .9589806{col 61}{space 1}    1.18{col 70}{space 3}0.239{col 78}{space 4} .6655332{col 91}{space 3} 5.117026
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.537274{col 50}{space 2} .7708171{col 61}{space 1}    0.86{col 70}{space 3}0.391{col 78}{space 4}  .574609{col 91}{space 3} 4.112729
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2}  1.39818{col 50}{space 2} .8245047{col 61}{space 1}    0.57{col 70}{space 3}0.570{col 78}{space 4} .4394799{col 91}{space 3} 4.448226
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.470139{col 50}{space 2} .4711989{col 61}{space 1}    1.20{col 70}{space 3}0.230{col 78}{space 4} .7837406{col 91}{space 3} 2.757685
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} .5774212{col 50}{space 2} .1611051{col 61}{space 1}   -1.97{col 70}{space 3}0.049{col 78}{space 4} .3339512{col 91}{space 3} .9983952
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 1.180449{col 50}{space 2} .6219186{col 61}{space 1}    0.31{col 70}{space 3}0.753{col 78}{space 4}  .419752{col 91}{space 3} 3.319724
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. 
. ******* Employment Sector Group ********
. 
. * Model 1: Worsen 
. svy: logit immigration_worsen i.employsector5 if infullmodel_TableD4 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   4{txt},{res}   3959{txt}){col 67}= {res}      5.32
{txt}{col 49}Prob > F{col 67}= {res}    0.0003

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                  immigration_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .6857264{col 50}{space 2} .0704072{col 61}{space 1}   -3.67{col 70}{space 3}0.000{col 78}{space 4} .5606951{col 91}{space 3} .8386388
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .4594494{col 50}{space 2}  .116612{col 61}{space 1}   -3.06{col 70}{space 3}0.002{col 78}{space 4} .2793379{col 91}{space 3} .7556933
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .8249088{col 50}{space 2} .1321991{col 61}{space 1}   -1.20{col 70}{space 3}0.230{col 78}{space 4} .6024923{col 91}{space 3} 1.129433
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2}  .590743{col 50}{space 2}  .181148{col 61}{space 1}   -1.72{col 70}{space 3}0.086{col 78}{space 4} .3238192{col 91}{space 3} 1.077692
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2901681{col 50}{space 2} .0183518{col 61}{space 1}  -19.56{col 70}{space 3}0.000{col 78}{space 4} .2563296{col 91}{space 3} .3284737
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit immigration_worsen i.employsector5 $controls_demographics if infullmodel_TableD4 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      7.86
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                  immigration_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .8324747{col 50}{space 2} .0880657{col 61}{space 1}   -1.73{col 70}{space 3}0.083{col 78}{space 4} .6765451{col 91}{space 3} 1.024343
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .5974396{col 50}{space 2} .1563604{col 61}{space 1}   -1.97{col 70}{space 3}0.049{col 78}{space 4} .3576446{col 91}{space 3} .9980136
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .7180797{col 50}{space 2}  .123263{col 61}{space 1}   -1.93{col 70}{space 3}0.054{col 78}{space 4} .5128777{col 91}{space 3} 1.005383
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5878524{col 50}{space 2} .1865065{col 61}{space 1}   -1.67{col 70}{space 3}0.094{col 78}{space 4} .3155928{col 91}{space 3} 1.094988
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.492692{col 50}{space 2} .2349607{col 61}{space 1}    2.54{col 70}{space 3}0.011{col 78}{space 4} 1.096336{col 91}{space 3}  2.03234
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.610056{col 50}{space 2} .2627392{col 61}{space 1}    2.92{col 70}{space 3}0.004{col 78}{space 4} 1.169214{col 91}{space 3} 2.217113
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .5958004{col 50}{space 2} .0580761{col 61}{space 1}   -5.31{col 70}{space 3}0.000{col 78}{space 4} .4921573{col 91}{space 3} .7212696
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7248211{col 50}{space 2}  .076892{col 61}{space 1}   -3.03{col 70}{space 3}0.002{col 78}{space 4} .5887139{col 91}{space 3} .8923955
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.042172{col 50}{space 2} .1260904{col 61}{space 1}    0.34{col 70}{space 3}0.733{col 78}{space 4} .8220966{col 91}{space 3} 1.321163
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .6709222{col 50}{space 2} .1149382{col 61}{space 1}   -2.33{col 70}{space 3}0.020{col 78}{space 4} .4795182{col 91}{space 3} .9387268
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .7103922{col 50}{space 2} .1180463{col 61}{space 1}   -2.06{col 70}{space 3}0.040{col 78}{space 4} .5128735{col 91}{space 3} .9839796
{txt}{space 34}4  {c |}{col 38}{res}{space 2}  .780437{col 50}{space 2} .1288428{col 61}{space 1}   -1.50{col 70}{space 3}0.133{col 78}{space 4} .5646373{col 91}{space 3} 1.078713
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .5720486{col 50}{space 2}  .100061{col 61}{space 1}   -3.19{col 70}{space 3}0.001{col 78}{space 4} .4059736{col 91}{space 3} .8060612
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .8561006{col 50}{space 2} .1220303{col 61}{space 1}   -1.09{col 70}{space 3}0.276{col 78}{space 4} .6473746{col 91}{space 3} 1.132124
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.278422{col 50}{space 2} .1836731{col 61}{space 1}    1.71{col 70}{space 3}0.087{col 78}{space 4} .9645912{col 91}{space 3} 1.694358
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.037038{col 50}{space 2} .1644867{col 61}{space 1}    0.23{col 70}{space 3}0.819{col 78}{space 4} .7598757{col 91}{space 3} 1.415294
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}   1.7023{col 50}{space 2} .2648454{col 61}{space 1}    3.42{col 70}{space 3}0.001{col 78}{space 4} 1.254772{col 91}{space 3} 2.309443
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .3355516{col 50}{space 2} .0770668{col 61}{space 1}   -4.75{col 70}{space 3}0.000{col 78}{space 4} .2138958{col 91}{space 3} .5264005
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit immigration_worsen i.employsector5 $controls_demographics $controls_objective if infullmodel_TableD4 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      6.22
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                  immigration_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2}  .822743{col 50}{space 2} .0896053{col 61}{space 1}   -1.79{col 70}{space 3}0.073{col 78}{space 4} .6645552{col 91}{space 3} 1.018585
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .6033868{col 50}{space 2} .1581631{col 61}{space 1}   -1.93{col 70}{space 3}0.054{col 78}{space 4} .3609159{col 91}{space 3} 1.008755
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .7067927{col 50}{space 2} .1216358{col 61}{space 1}   -2.02{col 70}{space 3}0.044{col 78}{space 4} .5043818{col 91}{space 3} .9904322
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5794473{col 50}{space 2} .1826008{col 61}{space 1}   -1.73{col 70}{space 3}0.083{col 78}{space 4} .3123873{col 91}{space 3} 1.074817
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.494482{col 50}{space 2} .2352497{col 61}{space 1}    2.55{col 70}{space 3}0.011{col 78}{space 4} 1.097641{col 91}{space 3} 2.034797
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.610733{col 50}{space 2} .2628841{col 61}{space 1}    2.92{col 70}{space 3}0.004{col 78}{space 4} 1.169657{col 91}{space 3} 2.218139
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}  .599543{col 50}{space 2} .0587938{col 61}{space 1}   -5.22{col 70}{space 3}0.000{col 78}{space 4} .4946776{col 91}{space 3} .7266385
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7254971{col 50}{space 2} .0774474{col 61}{space 1}   -3.01{col 70}{space 3}0.003{col 78}{space 4} .5884933{col 91}{space 3} .8943961
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.018511{col 50}{space 2} .1252356{col 61}{space 1}    0.15{col 70}{space 3}0.881{col 78}{space 4} .8003321{col 91}{space 3} 1.296167
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .6693483{col 50}{space 2} .1154689{col 61}{space 1}   -2.33{col 70}{space 3}0.020{col 78}{space 4} .4772732{col 91}{space 3} .9387227
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .7129928{col 50}{space 2} .1187527{col 61}{space 1}   -2.03{col 70}{space 3}0.042{col 78}{space 4} .5143631{col 91}{space 3} .9883268
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .7970378{col 50}{space 2} .1319035{col 61}{space 1}   -1.37{col 70}{space 3}0.171{col 78}{space 4}  .576194{col 91}{space 3} 1.102527
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .5898411{col 50}{space 2} .1036875{col 61}{space 1}   -3.00{col 70}{space 3}0.003{col 78}{space 4} .4178858{col 91}{space 3} .8325541
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .8419726{col 50}{space 2} .1299202{col 61}{space 1}   -1.11{col 70}{space 3}0.265{col 78}{space 4} .6221765{col 91}{space 3} 1.139416
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.219233{col 50}{space 2} .2865309{col 61}{space 1}    0.84{col 70}{space 3}0.399{col 78}{space 4}  .769105{col 91}{space 3} 1.932805
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .8823133{col 50}{space 2} .1578064{col 61}{space 1}   -0.70{col 70}{space 3}0.484{col 78}{space 4} .6213473{col 91}{space 3} 1.252885
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.365186{col 50}{space 2} .2966848{col 61}{space 1}    1.43{col 70}{space 3}0.152{col 78}{space 4} .8915596{col 91}{space 3} 2.090417
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .7326398{col 50}{space 2}   .08772{col 61}{space 1}   -2.60{col 70}{space 3}0.009{col 78}{space 4} .5793538{col 91}{space 3} .9264825
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.905536{col 50}{space 2} .4550764{col 61}{space 1}    2.70{col 70}{space 3}0.007{col 78}{space 4} 1.193089{col 91}{space 3} 3.043418
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2}  1.46295{col 50}{space 2} .3511115{col 61}{space 1}    1.59{col 70}{space 3}0.113{col 78}{space 4} .9138537{col 91}{space 3} 2.341975
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .8878147{col 50}{space 2} .2416446{col 61}{space 1}   -0.44{col 70}{space 3}0.662{col 78}{space 4} .5206817{col 91}{space 3} 1.513814
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.083534{col 50}{space 2} .1647442{col 61}{space 1}    0.53{col 70}{space 3}0.598{col 78}{space 4} .8042362{col 91}{space 3} 1.459828
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} .7759497{col 50}{space 2} .1117268{col 61}{space 1}   -1.76{col 70}{space 3}0.078{col 78}{space 4} .5851052{col 91}{space 3} 1.029042
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2}  .295907{col 50}{space 2} .0809559{col 61}{space 1}   -4.45{col 70}{space 3}0.000{col 78}{space 4} .1730643{col 91}{space 3} .5059447
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 2: Improve 
. svy: logit immigration_improve i.employsector5 if infullmodel_TableD4 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   4{txt},{res}   3959{txt}){col 67}= {res}      2.84
{txt}{col 49}Prob > F{col 67}= {res}    0.0229

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                 immigration_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2}  1.53388{col 50}{space 2} .2829527{col 61}{space 1}    2.32{col 70}{space 3}0.020{col 78}{space 4} 1.068374{col 91}{space 3} 2.202213
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.548819{col 50}{space 2} .4888714{col 61}{space 1}    1.39{col 70}{space 3}0.166{col 78}{space 4} .8341498{col 91}{space 3}  2.87579
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 2.024502{col 50}{space 2} .5192958{col 61}{space 1}    2.75{col 70}{space 3}0.006{col 78}{space 4} 1.224373{col 91}{space 3} 3.347517
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 2.459308{col 50}{space 2} 1.099572{col 61}{space 1}    2.01{col 70}{space 3}0.044{col 78}{space 4} 1.023574{col 91}{space 3} 5.908901
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .0411968{col 50}{space 2}  .005632{col 61}{space 1}  -23.33{col 70}{space 3}0.000{col 78}{space 4} .0315108{col 91}{space 3} .0538601
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit immigration_improve i.employsector5 $controls_demographics if infullmodel_TableD4 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      8.18
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                 immigration_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.493342{col 50}{space 2} .2813514{col 61}{space 1}    2.13{col 70}{space 3}0.033{col 78}{space 4} 1.032144{col 91}{space 3} 2.160617
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.580833{col 50}{space 2} .5289845{col 61}{space 1}    1.37{col 70}{space 3}0.171{col 78}{space 4} .8202885{col 91}{space 3}  3.04653
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 2.312422{col 50}{space 2} .6619176{col 61}{space 1}    2.93{col 70}{space 3}0.003{col 78}{space 4} 1.319292{col 91}{space 3} 4.053154
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2}  2.78285{col 50}{space 2} 1.382005{col 61}{space 1}    2.06{col 70}{space 3}0.039{col 78}{space 4} 1.051093{col 91}{space 3}  7.36781
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .5238864{col 50}{space 2} .1042154{col 61}{space 1}   -3.25{col 70}{space 3}0.001{col 78}{space 4} .3546975{col 91}{space 3} .7737776
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .2096789{col 50}{space 2}  .055851{col 61}{space 1}   -5.86{col 70}{space 3}0.000{col 78}{space 4} .1243815{col 91}{space 3} .3534709
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6261418{col 50}{space 2} .1042278{col 61}{space 1}   -2.81{col 70}{space 3}0.005{col 78}{space 4} .4517915{col 91}{space 3} .8677754
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.582045{col 50}{space 2} .2810231{col 61}{space 1}    2.58{col 70}{space 3}0.010{col 78}{space 4} 1.116789{col 91}{space 3} 2.241126
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .8706222{col 50}{space 2} .2078136{col 61}{space 1}   -0.58{col 70}{space 3}0.562{col 78}{space 4} .5452426{col 91}{space 3} 1.390176
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .3807654{col 50}{space 2} .1290625{col 61}{space 1}   -2.85{col 70}{space 3}0.004{col 78}{space 4}  .195907{col 91}{space 3} .7400566
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .3380461{col 50}{space 2} .0996209{col 61}{space 1}   -3.68{col 70}{space 3}0.000{col 78}{space 4} .1896939{col 91}{space 3} .6024189
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .2992312{col 50}{space 2} .0888934{col 61}{space 1}   -4.06{col 70}{space 3}0.000{col 78}{space 4} .1671325{col 91}{space 3} .5357385
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .3015157{col 50}{space 2} .0876506{col 61}{space 1}   -4.12{col 70}{space 3}0.000{col 78}{space 4} .1705257{col 91}{space 3}  .533126
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}  .677761{col 50}{space 2} .1575544{col 61}{space 1}   -1.67{col 70}{space 3}0.094{col 78}{space 4}  .429678{col 91}{space 3}  1.06908
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} .4170093{col 50}{space 2} .1227577{col 61}{space 1}   -2.97{col 70}{space 3}0.003{col 78}{space 4} .2341507{col 91}{space 3} .7426702
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .5801413{col 50}{space 2} .1648195{col 61}{space 1}   -1.92{col 70}{space 3}0.055{col 78}{space 4} .3323774{col 91}{space 3} 1.012596
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}  .192826{col 50}{space 2} .0788054{col 61}{space 1}   -4.03{col 70}{space 3}0.000{col 78}{space 4} .0865335{col 91}{space 3} .4296815
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .3295976{col 50}{space 2} .1196335{col 61}{space 1}   -3.06{col 70}{space 3}0.002{col 78}{space 4}  .161782{col 91}{space 3} .6714872
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit immigration_improve i.employsector5 $controls_demographics $controls_objective if infullmodel_TableD4 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      6.65
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                 immigration_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.530704{col 50}{space 2} .2892899{col 61}{space 1}    2.25{col 70}{space 3}0.024{col 78}{space 4}  1.05675{col 91}{space 3} 2.217227
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}   1.5984{col 50}{space 2} .5418812{col 61}{space 1}    1.38{col 70}{space 3}0.167{col 78}{space 4} .8222948{col 91}{space 3} 3.107017
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 2.379369{col 50}{space 2} .6929929{col 61}{space 1}    2.98{col 70}{space 3}0.003{col 78}{space 4} 1.344228{col 91}{space 3} 4.211634
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 2.843035{col 50}{space 2} 1.420095{col 61}{space 1}    2.09{col 70}{space 3}0.037{col 78}{space 4} 1.067769{col 91}{space 3} 7.569848
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .5450086{col 50}{space 2} .1092392{col 61}{space 1}   -3.03{col 70}{space 3}0.002{col 78}{space 4} .3679086{col 91}{space 3} .8073591
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .2160861{col 50}{space 2} .0575362{col 61}{space 1}   -5.75{col 70}{space 3}0.000{col 78}{space 4} .1282072{col 91}{space 3} .3642011
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6327077{col 50}{space 2} .1066214{col 61}{space 1}   -2.72{col 70}{space 3}0.007{col 78}{space 4} .4546929{col 91}{space 3} .8804165
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  1.59962{col 50}{space 2} .2886746{col 61}{space 1}    2.60{col 70}{space 3}0.009{col 78}{space 4} 1.122945{col 91}{space 3} 2.278639
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .8750304{col 50}{space 2} .2127652{col 61}{space 1}   -0.55{col 70}{space 3}0.583{col 78}{space 4} .5432362{col 91}{space 3} 1.409476
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .3855199{col 50}{space 2} .1295655{col 61}{space 1}   -2.84{col 70}{space 3}0.005{col 78}{space 4} .1994746{col 91}{space 3} .7450853
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .3391026{col 50}{space 2}   .10098{col 61}{space 1}   -3.63{col 70}{space 3}0.000{col 78}{space 4} .1891376{col 91}{space 3} .6079734
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .2984927{col 50}{space 2} .0892649{col 61}{space 1}   -4.04{col 70}{space 3}0.000{col 78}{space 4} .1660742{col 91}{space 3} .5364944
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .2989838{col 50}{space 2} .0867787{col 61}{space 1}   -4.16{col 70}{space 3}0.000{col 78}{space 4} .1692446{col 91}{space 3} .5281783
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .6383978{col 50}{space 2}  .155782{col 61}{space 1}   -1.84{col 70}{space 3}0.066{col 78}{space 4} .3956557{col 91}{space 3} 1.030067
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} .5915131{col 50}{space 2} .2114711{col 61}{space 1}   -1.47{col 70}{space 3}0.142{col 78}{space 4} .2934671{col 91}{space 3} 1.192256
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .6668142{col 50}{space 2} .2494675{col 61}{space 1}   -1.08{col 70}{space 3}0.279{col 78}{space 4} .3202265{col 91}{space 3} 1.388521
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .2398477{col 50}{space 2} .1187657{col 61}{space 1}   -2.88{col 70}{space 3}0.004{col 78}{space 4} .0908481{col 91}{space 3} .6332204
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.153029{col 50}{space 2} .2352344{col 61}{space 1}    0.70{col 70}{space 3}0.485{col 78}{space 4}  .772912{col 91}{space 3} 1.720088
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} .9666115{col 50}{space 2} .4463419{col 61}{space 1}   -0.07{col 70}{space 3}0.941{col 78}{space 4} .3909143{col 91}{space 3} 2.390134
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} .9426562{col 50}{space 2} .4153522{col 61}{space 1}   -0.13{col 70}{space 3}0.893{col 78}{space 4} .3973591{col 91}{space 3} 2.236266
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .5014466{col 50}{space 2} .2101865{col 61}{space 1}   -1.65{col 70}{space 3}0.100{col 78}{space 4}  .220458{col 91}{space 3} 1.140574
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .6785262{col 50}{space 2} .1817789{col 61}{space 1}   -1.45{col 70}{space 3}0.148{col 78}{space 4} .4012896{col 91}{space 3} 1.147296
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.120495{col 50}{space 2} .2439613{col 61}{space 1}    0.52{col 70}{space 3}0.601{col 78}{space 4} .7311801{col 91}{space 3}   1.7171
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2961112{col 50}{space 2} .1385063{col 61}{space 1}   -2.60{col 70}{space 3}0.009{col 78}{space 4}  .118354{col 91}{space 3} .7408443
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 3: Worsen v Improve
. svy: logit immigration_directionworse i.employsector5 if infullmodel_TableD4 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       907
{txt}{col 1}Number of PSUs{col 20}= {res}      907{txt}{col 49}Population size{col 67}={res} 1,006.7824
{txt}{col 49}Design df{col 67}= {res}       906
{txt}{col 49}F({res}   4{txt},{res}    903{txt}){col 67}= {res}      5.12
{txt}{col 49}Prob > F{col 67}= {res}    0.0004

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}          immigration_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .4912174{col 50}{space 2} .0997151{col 61}{space 1}   -3.50{col 70}{space 3}0.000{col 78}{space 4} .3298012{col 91}{space 3} .7316363
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}  .345032{col 50}{space 2} .1344518{col 61}{space 1}   -2.73{col 70}{space 3}0.006{col 78}{space 4} .1605906{col 91}{space 3} .7413077
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .4413599{col 50}{space 2} .1273946{col 61}{space 1}   -2.83{col 70}{space 3}0.005{col 78}{space 4} .2504797{col 91}{space 3} .7777021
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .2798338{col 50}{space 2} .1434665{col 61}{space 1}   -2.48{col 70}{space 3}0.013{col 78}{space 4} .1023101{col 91}{space 3} .7653887
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 5.684242{col 50}{space 2} .8285859{col 61}{space 1}   11.92{col 70}{space 3}0.000{col 78}{space 4}     4.27{col 91}{space 3} 7.566888
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit immigration_directionworse i.employsector5 $controls_demographics if infullmodel_TableD4 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       907
{txt}{col 1}Number of PSUs{col 20}= {res}      907{txt}{col 49}Population size{col 67}={res} 1,006.7824
{txt}{col 49}Design df{col 67}= {res}       906
{txt}{col 49}F({res}  17{txt},{res}    890{txt}){col 67}= {res}      8.38
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}          immigration_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .6242586{col 50}{space 2}   .13483{col 61}{space 1}   -2.18{col 70}{space 3}0.029{col 78}{space 4} .4085756{col 91}{space 3} .9537984
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}  .373052{col 50}{space 2} .1877147{col 61}{space 1}   -1.96{col 70}{space 3}0.050{col 78}{space 4}  .138958{col 91}{space 3}  1.00151
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .3478355{col 50}{space 2} .1153555{col 61}{space 1}   -3.18{col 70}{space 3}0.002{col 78}{space 4} .1814284{col 91}{space 3}  .666872
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .1620861{col 50}{space 2} .1142552{col 61}{space 1}   -2.58{col 70}{space 3}0.010{col 78}{space 4} .0406376{col 91}{space 3} .6464936
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 2.527205{col 50}{space 2} .6544328{col 61}{space 1}    3.58{col 70}{space 3}0.000{col 78}{space 4} 1.520277{col 91}{space 3} 4.201055
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 7.166644{col 50}{space 2} 2.219853{col 61}{space 1}    6.36{col 70}{space 3}0.000{col 78}{space 4} 3.902162{col 91}{space 3} 13.16214
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .8566872{col 50}{space 2} .1800572{col 61}{space 1}   -0.74{col 70}{space 3}0.462{col 78}{space 4} .5671249{col 91}{space 3} 1.294094
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .5189287{col 50}{space 2} .1065262{col 61}{space 1}   -3.20{col 70}{space 3}0.001{col 78}{space 4} .3468474{col 91}{space 3} .7763846
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.018112{col 50}{space 2} .2694679{col 61}{space 1}    0.07{col 70}{space 3}0.946{col 78}{space 4}  .605624{col 91}{space 3} 1.711543
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.480791{col 50}{space 2} .5637549{col 61}{space 1}    1.03{col 70}{space 3}0.303{col 78}{space 4} .7014516{col 91}{space 3} 3.126007
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.469768{col 50}{space 2} .5058497{col 61}{space 1}    1.12{col 70}{space 3}0.263{col 78}{space 4} .7479959{col 91}{space 3} 2.888009
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.895567{col 50}{space 2} .6429994{col 61}{space 1}    1.89{col 70}{space 3}0.060{col 78}{space 4} .9741258{col 91}{space 3} 3.688615
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.503835{col 50}{space 2}  .508504{col 61}{space 1}    1.21{col 70}{space 3}0.228{col 78}{space 4} .7744465{col 91}{space 3} 2.920175
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.231503{col 50}{space 2} .3397895{col 61}{space 1}    0.75{col 70}{space 3}0.451{col 78}{space 4} .7165767{col 91}{space 3} 2.116453
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}  3.19873{col 50}{space 2} 1.102565{col 61}{space 1}    3.37{col 70}{space 3}0.001{col 78}{space 4} 1.626245{col 91}{space 3}  6.29172
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.799199{col 50}{space 2} .6013717{col 61}{space 1}    1.76{col 70}{space 3}0.079{col 78}{space 4} .9336613{col 91}{space 3}  3.46712
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}  7.46925{col 50}{space 2} 2.924152{col 61}{space 1}    5.14{col 70}{space 3}0.000{col 78}{space 4} 3.464117{col 91}{space 3} 16.10503
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 1.189334{col 50}{space 2} .4885172{col 61}{space 1}    0.42{col 70}{space 3}0.673{col 78}{space 4} .5311367{col 91}{space 3} 2.663185
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit immigration_directionworse i.employsector5 $controls_demographics $controls_objective if infullmodel_TableD4 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       907
{txt}{col 1}Number of PSUs{col 20}= {res}      907{txt}{col 49}Population size{col 67}={res} 1,006.7824
{txt}{col 49}Design df{col 67}= {res}       906
{txt}{col 49}F({res}  23{txt},{res}    884{txt}){col 67}= {res}      6.72
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}          immigration_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .5824175{col 50}{space 2}  .127716{col 61}{space 1}   -2.47{col 70}{space 3}0.014{col 78}{space 4} .3787287{col 91}{space 3} .8956549
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .3381561{col 50}{space 2}  .175906{col 61}{space 1}   -2.08{col 70}{space 3}0.037{col 78}{space 4} .1218251{col 91}{space 3} .9386368
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .3337968{col 50}{space 2} .1166511{col 61}{space 1}   -3.14{col 70}{space 3}0.002{col 78}{space 4} .1681191{col 91}{space 3} .6627462
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .1477794{col 50}{space 2} .1025179{col 61}{space 1}   -2.76{col 70}{space 3}0.006{col 78}{space 4} .0378727{col 91}{space 3} .5766358
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 2.559466{col 50}{space 2} .6820355{col 61}{space 1}    3.53{col 70}{space 3}0.000{col 78}{space 4} 1.517125{col 91}{space 3} 4.317947
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 7.746819{col 50}{space 2} 2.422385{col 61}{space 1}    6.55{col 70}{space 3}0.000{col 78}{space 4} 4.193741{col 91}{space 3} 14.31018
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .8649187{col 50}{space 2} .1869625{col 61}{space 1}   -0.67{col 70}{space 3}0.502{col 78}{space 4} .5658897{col 91}{space 3} 1.321962
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  .516291{col 50}{space 2} .1088358{col 61}{space 1}   -3.14{col 70}{space 3}0.002{col 78}{space 4} .3413647{col 91}{space 3} .7808553
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.032428{col 50}{space 2} .2875799{col 61}{space 1}    0.11{col 70}{space 3}0.909{col 78}{space 4} .5976447{col 91}{space 3} 1.783515
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}  1.51432{col 50}{space 2} .5844268{col 61}{space 1}    1.08{col 70}{space 3}0.283{col 78}{space 4} .7100206{col 91}{space 3} 3.229716
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.333983{col 50}{space 2}  .476722{col 61}{space 1}    0.81{col 70}{space 3}0.420{col 78}{space 4}  .661533{col 91}{space 3} 2.689979
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.908907{col 50}{space 2}  .665307{col 61}{space 1}    1.86{col 70}{space 3}0.064{col 78}{space 4} .9632094{col 91}{space 3}  3.78311
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.494628{col 50}{space 2} .5094426{col 61}{space 1}    1.18{col 70}{space 3}0.239{col 78}{space 4} .7656208{col 91}{space 3}  2.91778
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.441223{col 50}{space 2} .4333198{col 61}{space 1}    1.22{col 70}{space 3}0.224{col 78}{space 4} .7988503{col 91}{space 3}  2.60014
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 2.484735{col 50}{space 2} 1.243821{col 61}{space 1}    1.82{col 70}{space 3}0.069{col 78}{space 4}  .930275{col 91}{space 3} 6.636646
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.433121{col 50}{space 2} .5973314{col 61}{space 1}    0.86{col 70}{space 3}0.388{col 78}{space 4} .6324459{col 91}{space 3} 3.247448
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 5.020165{col 50}{space 2} 2.570767{col 61}{space 1}    3.15{col 70}{space 3}0.002{col 78}{space 4} 1.837574{col 91}{space 3} 13.71486
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .6914669{col 50}{space 2} .1766033{col 61}{space 1}   -1.44{col 70}{space 3}0.149{col 78}{space 4} .4188712{col 91}{space 3} 1.141464
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.845413{col 50}{space 2} .9589806{col 61}{space 1}    1.18{col 70}{space 3}0.239{col 78}{space 4} .6655332{col 91}{space 3} 5.117026
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.537274{col 50}{space 2} .7708171{col 61}{space 1}    0.86{col 70}{space 3}0.391{col 78}{space 4}  .574609{col 91}{space 3} 4.112729
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2}  1.39818{col 50}{space 2} .8245047{col 61}{space 1}    0.57{col 70}{space 3}0.570{col 78}{space 4} .4394799{col 91}{space 3} 4.448226
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.470139{col 50}{space 2} .4711989{col 61}{space 1}    1.20{col 70}{space 3}0.230{col 78}{space 4} .7837406{col 91}{space 3} 2.757685
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} .5774212{col 50}{space 2} .1611051{col 61}{space 1}   -1.97{col 70}{space 3}0.049{col 78}{space 4} .3339512{col 91}{space 3} .9983952
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 1.180449{col 50}{space 2} .6219186{col 61}{space 1}    0.31{col 70}{space 3}0.753{col 78}{space 4}  .419752{col 91}{space 3} 3.319724
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. 
. ******* Equivalised HH Income ********
. 
. 
. * Model 1: Worsen 
. svy: logit immigration_worsen i.equivincomeHHquintile_HBAI_imp if infullmodel_TableD4 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   4{txt},{res}   3959{txt}){col 67}= {res}      6.24
{txt}{col 49}Prob > F{col 67}= {res}    0.0001

{txt}{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 32}{c |}{col 44}  Linearized
{col 1}            immigration_worsen{col 32}{c |} Odds Ratio{col 44}   Std. Err.{col 56}      t{col 64}   P>|t|{col 72}     [95% Con{col 85}f. Interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
equivincomeHHquintile_HBAI_imp {c |}
{space 28}2  {c |}{col 32}{res}{space 2} .6734345{col 44}{space 2} .1115484{col 55}{space 1}   -2.39{col 64}{space 3}0.017{col 72}{space 4} .4866966{col 85}{space 3} .9318209
{txt}{space 28}3  {c |}{col 32}{res}{space 2} .6667052{col 44}{space 2} .1068104{col 55}{space 1}   -2.53{col 64}{space 3}0.011{col 72}{space 4} .4869947{col 85}{space 3} .9127323
{txt}{space 28}4  {c |}{col 32}{res}{space 2} .7266271{col 44}{space 2} .1110092{col 55}{space 1}   -2.09{col 64}{space 3}0.037{col 72}{space 4} .5385561{col 85}{space 3} .9803749
{txt}{space 17}Top Quintile  {c |}{col 32}{res}{space 2} .4820723{col 44}{space 2} .0728187{col 55}{space 1}   -4.83{col 64}{space 3}0.000{col 72}{space 4} .3585056{col 85}{space 3} .6482289
{txt}{space 30} {c |}
{space 25}_cons {c |}{col 32}{res}{space 2} .3645372{col 44}{space 2} .0441784{col 55}{space 1}   -8.33{col 64}{space 3}0.000{col 72}{space 4} .2874438{col 85}{space 3} .4623074
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit immigration_worsen i.equivincomeHHquintile_HBAI_imp $controls_demographics if infullmodel_TableD4 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      7.86
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                  immigration_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .6709222{col 50}{space 2} .1149382{col 61}{space 1}   -2.33{col 70}{space 3}0.020{col 78}{space 4} .4795182{col 91}{space 3} .9387268
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .7103922{col 50}{space 2} .1180463{col 61}{space 1}   -2.06{col 70}{space 3}0.040{col 78}{space 4} .5128735{col 91}{space 3} .9839796
{txt}{space 34}4  {c |}{col 38}{res}{space 2}  .780437{col 50}{space 2} .1288428{col 61}{space 1}   -1.50{col 70}{space 3}0.133{col 78}{space 4} .5646373{col 91}{space 3} 1.078713
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .5720486{col 50}{space 2}  .100061{col 61}{space 1}   -3.19{col 70}{space 3}0.001{col 78}{space 4} .4059736{col 91}{space 3} .8060612
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.492692{col 50}{space 2} .2349607{col 61}{space 1}    2.54{col 70}{space 3}0.011{col 78}{space 4} 1.096336{col 91}{space 3}  2.03234
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.610056{col 50}{space 2} .2627392{col 61}{space 1}    2.92{col 70}{space 3}0.004{col 78}{space 4} 1.169214{col 91}{space 3} 2.217113
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .5958004{col 50}{space 2} .0580761{col 61}{space 1}   -5.31{col 70}{space 3}0.000{col 78}{space 4} .4921573{col 91}{space 3} .7212696
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7248211{col 50}{space 2}  .076892{col 61}{space 1}   -3.03{col 70}{space 3}0.002{col 78}{space 4} .5887139{col 91}{space 3} .8923955
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.042172{col 50}{space 2} .1260904{col 61}{space 1}    0.34{col 70}{space 3}0.733{col 78}{space 4} .8220966{col 91}{space 3} 1.321163
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .8324747{col 50}{space 2} .0880657{col 61}{space 1}   -1.73{col 70}{space 3}0.083{col 78}{space 4} .6765451{col 91}{space 3} 1.024343
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .5974396{col 50}{space 2} .1563604{col 61}{space 1}   -1.97{col 70}{space 3}0.049{col 78}{space 4} .3576446{col 91}{space 3} .9980136
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .7180797{col 50}{space 2}  .123263{col 61}{space 1}   -1.93{col 70}{space 3}0.054{col 78}{space 4} .5128777{col 91}{space 3} 1.005383
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5878524{col 50}{space 2} .1865065{col 61}{space 1}   -1.67{col 70}{space 3}0.094{col 78}{space 4} .3155928{col 91}{space 3} 1.094988
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .8561006{col 50}{space 2} .1220303{col 61}{space 1}   -1.09{col 70}{space 3}0.276{col 78}{space 4} .6473746{col 91}{space 3} 1.132124
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.278422{col 50}{space 2} .1836731{col 61}{space 1}    1.71{col 70}{space 3}0.087{col 78}{space 4} .9645912{col 91}{space 3} 1.694358
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.037038{col 50}{space 2} .1644867{col 61}{space 1}    0.23{col 70}{space 3}0.819{col 78}{space 4} .7598757{col 91}{space 3} 1.415294
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}   1.7023{col 50}{space 2} .2648454{col 61}{space 1}    3.42{col 70}{space 3}0.001{col 78}{space 4} 1.254772{col 91}{space 3} 2.309443
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .3355516{col 50}{space 2} .0770668{col 61}{space 1}   -4.75{col 70}{space 3}0.000{col 78}{space 4} .2138958{col 91}{space 3} .5264005
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit immigration_worsen i.equivincomeHHquintile_HBAI_imp $controls_demographics $controls_objective if infullmodel_TableD4 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      6.22
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                  immigration_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .6693483{col 50}{space 2} .1154689{col 61}{space 1}   -2.33{col 70}{space 3}0.020{col 78}{space 4} .4772732{col 91}{space 3} .9387227
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .7129928{col 50}{space 2} .1187527{col 61}{space 1}   -2.03{col 70}{space 3}0.042{col 78}{space 4} .5143631{col 91}{space 3} .9883268
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .7970378{col 50}{space 2} .1319035{col 61}{space 1}   -1.37{col 70}{space 3}0.171{col 78}{space 4}  .576194{col 91}{space 3} 1.102527
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .5898411{col 50}{space 2} .1036875{col 61}{space 1}   -3.00{col 70}{space 3}0.003{col 78}{space 4} .4178858{col 91}{space 3} .8325541
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.494482{col 50}{space 2} .2352497{col 61}{space 1}    2.55{col 70}{space 3}0.011{col 78}{space 4} 1.097641{col 91}{space 3} 2.034797
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.610733{col 50}{space 2} .2628841{col 61}{space 1}    2.92{col 70}{space 3}0.004{col 78}{space 4} 1.169657{col 91}{space 3} 2.218139
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}  .599543{col 50}{space 2} .0587938{col 61}{space 1}   -5.22{col 70}{space 3}0.000{col 78}{space 4} .4946776{col 91}{space 3} .7266385
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7254971{col 50}{space 2} .0774474{col 61}{space 1}   -3.01{col 70}{space 3}0.003{col 78}{space 4} .5884933{col 91}{space 3} .8943961
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.018511{col 50}{space 2} .1252356{col 61}{space 1}    0.15{col 70}{space 3}0.881{col 78}{space 4} .8003321{col 91}{space 3} 1.296167
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2}  .822743{col 50}{space 2} .0896053{col 61}{space 1}   -1.79{col 70}{space 3}0.073{col 78}{space 4} .6645552{col 91}{space 3} 1.018585
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .6033868{col 50}{space 2} .1581631{col 61}{space 1}   -1.93{col 70}{space 3}0.054{col 78}{space 4} .3609159{col 91}{space 3} 1.008755
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .7067927{col 50}{space 2} .1216358{col 61}{space 1}   -2.02{col 70}{space 3}0.044{col 78}{space 4} .5043818{col 91}{space 3} .9904322
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5794473{col 50}{space 2} .1826008{col 61}{space 1}   -1.73{col 70}{space 3}0.083{col 78}{space 4} .3123873{col 91}{space 3} 1.074817
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .8419726{col 50}{space 2} .1299202{col 61}{space 1}   -1.11{col 70}{space 3}0.265{col 78}{space 4} .6221765{col 91}{space 3} 1.139416
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.219233{col 50}{space 2} .2865309{col 61}{space 1}    0.84{col 70}{space 3}0.399{col 78}{space 4}  .769105{col 91}{space 3} 1.932805
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .8823133{col 50}{space 2} .1578064{col 61}{space 1}   -0.70{col 70}{space 3}0.484{col 78}{space 4} .6213473{col 91}{space 3} 1.252885
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.365186{col 50}{space 2} .2966848{col 61}{space 1}    1.43{col 70}{space 3}0.152{col 78}{space 4} .8915596{col 91}{space 3} 2.090417
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .7326398{col 50}{space 2}   .08772{col 61}{space 1}   -2.60{col 70}{space 3}0.009{col 78}{space 4} .5793538{col 91}{space 3} .9264825
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.905536{col 50}{space 2} .4550764{col 61}{space 1}    2.70{col 70}{space 3}0.007{col 78}{space 4} 1.193089{col 91}{space 3} 3.043418
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2}  1.46295{col 50}{space 2} .3511115{col 61}{space 1}    1.59{col 70}{space 3}0.113{col 78}{space 4} .9138537{col 91}{space 3} 2.341975
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .8878147{col 50}{space 2} .2416446{col 61}{space 1}   -0.44{col 70}{space 3}0.662{col 78}{space 4} .5206817{col 91}{space 3} 1.513814
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.083534{col 50}{space 2} .1647442{col 61}{space 1}    0.53{col 70}{space 3}0.598{col 78}{space 4} .8042362{col 91}{space 3} 1.459828
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} .7759497{col 50}{space 2} .1117268{col 61}{space 1}   -1.76{col 70}{space 3}0.078{col 78}{space 4} .5851052{col 91}{space 3} 1.029042
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2}  .295907{col 50}{space 2} .0809559{col 61}{space 1}   -4.45{col 70}{space 3}0.000{col 78}{space 4} .1730643{col 91}{space 3} .5059447
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 2: Improve 
. svy: logit immigration_improve i.equivincomeHHquintile_HBAI_imp if infullmodel_TableD4 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   4{txt},{res}   3959{txt}){col 67}= {res}      3.38
{txt}{col 49}Prob > F{col 67}= {res}    0.0090

{txt}{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 32}{c |}{col 44}  Linearized
{col 1}           immigration_improve{col 32}{c |} Odds Ratio{col 44}   Std. Err.{col 56}      t{col 64}   P>|t|{col 72}     [95% Con{col 85}f. Interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
equivincomeHHquintile_HBAI_imp {c |}
{space 28}2  {c |}{col 32}{res}{space 2} .4004612{col 44}{space 2} .1285909{col 55}{space 1}   -2.85{col 64}{space 3}0.004{col 72}{space 4} .2133783{col 85}{space 3} .7515724
{txt}{space 28}3  {c |}{col 32}{res}{space 2} .4569195{col 44}{space 2} .1309319{col 55}{space 1}   -2.73{col 64}{space 3}0.006{col 72}{space 4} .2605256{col 85}{space 3} .8013625
{txt}{space 28}4  {c |}{col 32}{res}{space 2} .4372999{col 44}{space 2} .1143426{col 55}{space 1}   -3.16{col 64}{space 3}0.002{col 72}{space 4} .2619052{col 85}{space 3} .7301543
{txt}{space 17}Top Quintile  {c |}{col 32}{res}{space 2} .5994517{col 44}{space 2} .1437135{col 55}{space 1}   -2.13{col 64}{space 3}0.033{col 72}{space 4}  .374648{col 85}{space 3} .9591465
{txt}{space 30} {c |}
{space 25}_cons {c |}{col 32}{res}{space 2} .1009026{col 44}{space 2} .0203559{col 55}{space 1}  -11.37{col 64}{space 3}0.000{col 72}{space 4} .0679407{col 85}{space 3} .1498561
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit immigration_improve i.equivincomeHHquintile_HBAI_imp $controls_demographics if infullmodel_TableD4 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      8.18
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                 immigration_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .3807654{col 50}{space 2} .1290625{col 61}{space 1}   -2.85{col 70}{space 3}0.004{col 78}{space 4}  .195907{col 91}{space 3} .7400566
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .3380461{col 50}{space 2} .0996209{col 61}{space 1}   -3.68{col 70}{space 3}0.000{col 78}{space 4} .1896939{col 91}{space 3} .6024189
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .2992312{col 50}{space 2} .0888934{col 61}{space 1}   -4.06{col 70}{space 3}0.000{col 78}{space 4} .1671325{col 91}{space 3} .5357385
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .3015157{col 50}{space 2} .0876506{col 61}{space 1}   -4.12{col 70}{space 3}0.000{col 78}{space 4} .1705257{col 91}{space 3}  .533126
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .5238864{col 50}{space 2} .1042154{col 61}{space 1}   -3.25{col 70}{space 3}0.001{col 78}{space 4} .3546975{col 91}{space 3} .7737776
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .2096789{col 50}{space 2}  .055851{col 61}{space 1}   -5.86{col 70}{space 3}0.000{col 78}{space 4} .1243815{col 91}{space 3} .3534709
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6261418{col 50}{space 2} .1042278{col 61}{space 1}   -2.81{col 70}{space 3}0.005{col 78}{space 4} .4517915{col 91}{space 3} .8677754
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.582045{col 50}{space 2} .2810231{col 61}{space 1}    2.58{col 70}{space 3}0.010{col 78}{space 4} 1.116789{col 91}{space 3} 2.241126
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .8706222{col 50}{space 2} .2078136{col 61}{space 1}   -0.58{col 70}{space 3}0.562{col 78}{space 4} .5452426{col 91}{space 3} 1.390176
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.493342{col 50}{space 2} .2813514{col 61}{space 1}    2.13{col 70}{space 3}0.033{col 78}{space 4} 1.032144{col 91}{space 3} 2.160617
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.580833{col 50}{space 2} .5289845{col 61}{space 1}    1.37{col 70}{space 3}0.171{col 78}{space 4} .8202885{col 91}{space 3}  3.04653
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 2.312422{col 50}{space 2} .6619176{col 61}{space 1}    2.93{col 70}{space 3}0.003{col 78}{space 4} 1.319292{col 91}{space 3} 4.053154
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2}  2.78285{col 50}{space 2} 1.382005{col 61}{space 1}    2.06{col 70}{space 3}0.039{col 78}{space 4} 1.051093{col 91}{space 3}  7.36781
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}  .677761{col 50}{space 2} .1575544{col 61}{space 1}   -1.67{col 70}{space 3}0.094{col 78}{space 4}  .429678{col 91}{space 3}  1.06908
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} .4170093{col 50}{space 2} .1227577{col 61}{space 1}   -2.97{col 70}{space 3}0.003{col 78}{space 4} .2341507{col 91}{space 3} .7426702
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .5801413{col 50}{space 2} .1648195{col 61}{space 1}   -1.92{col 70}{space 3}0.055{col 78}{space 4} .3323774{col 91}{space 3} 1.012596
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}  .192826{col 50}{space 2} .0788054{col 61}{space 1}   -4.03{col 70}{space 3}0.000{col 78}{space 4} .0865335{col 91}{space 3} .4296815
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .3295976{col 50}{space 2} .1196335{col 61}{space 1}   -3.06{col 70}{space 3}0.002{col 78}{space 4}  .161782{col 91}{space 3} .6714872
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit immigration_improve i.equivincomeHHquintile_HBAI_imp $controls_demographics $controls_objective if infullmodel_TableD4 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      6.65
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                 immigration_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .3855199{col 50}{space 2} .1295655{col 61}{space 1}   -2.84{col 70}{space 3}0.005{col 78}{space 4} .1994746{col 91}{space 3} .7450853
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .3391026{col 50}{space 2}   .10098{col 61}{space 1}   -3.63{col 70}{space 3}0.000{col 78}{space 4} .1891376{col 91}{space 3} .6079734
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .2984927{col 50}{space 2} .0892649{col 61}{space 1}   -4.04{col 70}{space 3}0.000{col 78}{space 4} .1660742{col 91}{space 3} .5364944
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .2989838{col 50}{space 2} .0867787{col 61}{space 1}   -4.16{col 70}{space 3}0.000{col 78}{space 4} .1692446{col 91}{space 3} .5281783
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .5450086{col 50}{space 2} .1092392{col 61}{space 1}   -3.03{col 70}{space 3}0.002{col 78}{space 4} .3679086{col 91}{space 3} .8073591
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .2160861{col 50}{space 2} .0575362{col 61}{space 1}   -5.75{col 70}{space 3}0.000{col 78}{space 4} .1282072{col 91}{space 3} .3642011
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6327077{col 50}{space 2} .1066214{col 61}{space 1}   -2.72{col 70}{space 3}0.007{col 78}{space 4} .4546929{col 91}{space 3} .8804165
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  1.59962{col 50}{space 2} .2886746{col 61}{space 1}    2.60{col 70}{space 3}0.009{col 78}{space 4} 1.122945{col 91}{space 3} 2.278639
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .8750304{col 50}{space 2} .2127652{col 61}{space 1}   -0.55{col 70}{space 3}0.583{col 78}{space 4} .5432362{col 91}{space 3} 1.409476
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.530704{col 50}{space 2} .2892899{col 61}{space 1}    2.25{col 70}{space 3}0.024{col 78}{space 4}  1.05675{col 91}{space 3} 2.217227
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}   1.5984{col 50}{space 2} .5418812{col 61}{space 1}    1.38{col 70}{space 3}0.167{col 78}{space 4} .8222948{col 91}{space 3} 3.107017
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 2.379369{col 50}{space 2} .6929929{col 61}{space 1}    2.98{col 70}{space 3}0.003{col 78}{space 4} 1.344228{col 91}{space 3} 4.211634
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 2.843035{col 50}{space 2} 1.420095{col 61}{space 1}    2.09{col 70}{space 3}0.037{col 78}{space 4} 1.067769{col 91}{space 3} 7.569848
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .6383978{col 50}{space 2}  .155782{col 61}{space 1}   -1.84{col 70}{space 3}0.066{col 78}{space 4} .3956557{col 91}{space 3} 1.030067
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} .5915131{col 50}{space 2} .2114711{col 61}{space 1}   -1.47{col 70}{space 3}0.142{col 78}{space 4} .2934671{col 91}{space 3} 1.192256
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .6668142{col 50}{space 2} .2494675{col 61}{space 1}   -1.08{col 70}{space 3}0.279{col 78}{space 4} .3202265{col 91}{space 3} 1.388521
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .2398477{col 50}{space 2} .1187657{col 61}{space 1}   -2.88{col 70}{space 3}0.004{col 78}{space 4} .0908481{col 91}{space 3} .6332204
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.153029{col 50}{space 2} .2352344{col 61}{space 1}    0.70{col 70}{space 3}0.485{col 78}{space 4}  .772912{col 91}{space 3} 1.720088
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} .9666115{col 50}{space 2} .4463419{col 61}{space 1}   -0.07{col 70}{space 3}0.941{col 78}{space 4} .3909143{col 91}{space 3} 2.390134
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} .9426562{col 50}{space 2} .4153522{col 61}{space 1}   -0.13{col 70}{space 3}0.893{col 78}{space 4} .3973591{col 91}{space 3} 2.236266
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .5014466{col 50}{space 2} .2101865{col 61}{space 1}   -1.65{col 70}{space 3}0.100{col 78}{space 4}  .220458{col 91}{space 3} 1.140574
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .6785262{col 50}{space 2} .1817789{col 61}{space 1}   -1.45{col 70}{space 3}0.148{col 78}{space 4} .4012896{col 91}{space 3} 1.147296
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.120495{col 50}{space 2} .2439613{col 61}{space 1}    0.52{col 70}{space 3}0.601{col 78}{space 4} .7311801{col 91}{space 3}   1.7171
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2961112{col 50}{space 2} .1385063{col 61}{space 1}   -2.60{col 70}{space 3}0.009{col 78}{space 4}  .118354{col 91}{space 3} .7408443
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 3: Worsen v Improve
. svy: logit immigration_directionworse i.equivincomeHHquintile_HBAI_imp if infullmodel_TableD4 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       907
{txt}{col 1}Number of PSUs{col 20}= {res}      907{txt}{col 49}Population size{col 67}={res} 1,006.7824
{txt}{col 49}Design df{col 67}= {res}       906
{txt}{col 49}F({res}   4{txt},{res}    903{txt}){col 67}= {res}      2.72
{txt}{col 49}Prob > F{col 67}= {res}    0.0284

{txt}{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 32}{c |}{col 44}  Linearized
{col 1}    immigration_directionworse{col 32}{c |} Odds Ratio{col 44}   Std. Err.{col 56}      t{col 64}   P>|t|{col 72}     [95% Con{col 85}f. Interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
equivincomeHHquintile_HBAI_imp {c |}
{space 28}2  {c |}{col 32}{res}{space 2} 1.741141{col 44}{space 2}  .602262{col 55}{space 1}    1.60{col 64}{space 3}0.109{col 72}{space 4}  .883098{col 85}{space 3} 3.432884
{txt}{space 28}3  {c |}{col 32}{res}{space 2} 1.522022{col 44}{space 2} .4754853{col 55}{space 1}    1.34{col 64}{space 3}0.179{col 72}{space 4} .8244167{col 85}{space 3} 2.809928
{txt}{space 28}4  {c |}{col 32}{res}{space 2} 1.700086{col 44}{space 2} .4885789{col 55}{space 1}    1.85{col 64}{space 3}0.065{col 72}{space 4} .9672111{col 85}{space 3} 2.988275
{txt}{space 17}Top Quintile  {c |}{col 32}{res}{space 2} .8990642{col 44}{space 2} .2409458{col 55}{space 1}   -0.40{col 64}{space 3}0.691{col 72}{space 4}  .531333{col 85}{space 3} 1.521299
{txt}{space 30} {c |}
{space 25}_cons {c |}{col 32}{res}{space 2} 2.914761{col 44}{space 2} .6451726{col 55}{space 1}    4.83{col 64}{space 3}0.000{col 72}{space 4} 1.887732{col 85}{space 3}  4.50055
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit immigration_directionworse i.equivincomeHHquintile_HBAI_imp $controls_demographics if infullmodel_TableD4 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       907
{txt}{col 1}Number of PSUs{col 20}= {res}      907{txt}{col 49}Population size{col 67}={res} 1,006.7824
{txt}{col 49}Design df{col 67}= {res}       906
{txt}{col 49}F({res}  17{txt},{res}    890{txt}){col 67}= {res}      8.38
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}          immigration_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.480791{col 50}{space 2} .5637549{col 61}{space 1}    1.03{col 70}{space 3}0.303{col 78}{space 4} .7014516{col 91}{space 3} 3.126007
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.469768{col 50}{space 2} .5058497{col 61}{space 1}    1.12{col 70}{space 3}0.263{col 78}{space 4} .7479959{col 91}{space 3} 2.888009
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.895567{col 50}{space 2} .6429994{col 61}{space 1}    1.89{col 70}{space 3}0.060{col 78}{space 4} .9741258{col 91}{space 3} 3.688615
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.503835{col 50}{space 2}  .508504{col 61}{space 1}    1.21{col 70}{space 3}0.228{col 78}{space 4} .7744465{col 91}{space 3} 2.920175
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 2.527205{col 50}{space 2} .6544328{col 61}{space 1}    3.58{col 70}{space 3}0.000{col 78}{space 4} 1.520277{col 91}{space 3} 4.201055
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 7.166644{col 50}{space 2} 2.219853{col 61}{space 1}    6.36{col 70}{space 3}0.000{col 78}{space 4} 3.902162{col 91}{space 3} 13.16214
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .8566872{col 50}{space 2} .1800572{col 61}{space 1}   -0.74{col 70}{space 3}0.462{col 78}{space 4} .5671249{col 91}{space 3} 1.294094
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .5189287{col 50}{space 2} .1065262{col 61}{space 1}   -3.20{col 70}{space 3}0.001{col 78}{space 4} .3468474{col 91}{space 3} .7763846
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.018112{col 50}{space 2} .2694679{col 61}{space 1}    0.07{col 70}{space 3}0.946{col 78}{space 4}  .605624{col 91}{space 3} 1.711543
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .6242586{col 50}{space 2}   .13483{col 61}{space 1}   -2.18{col 70}{space 3}0.029{col 78}{space 4} .4085756{col 91}{space 3} .9537984
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}  .373052{col 50}{space 2} .1877147{col 61}{space 1}   -1.96{col 70}{space 3}0.050{col 78}{space 4}  .138958{col 91}{space 3}  1.00151
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .3478355{col 50}{space 2} .1153555{col 61}{space 1}   -3.18{col 70}{space 3}0.002{col 78}{space 4} .1814284{col 91}{space 3}  .666872
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .1620861{col 50}{space 2} .1142552{col 61}{space 1}   -2.58{col 70}{space 3}0.010{col 78}{space 4} .0406376{col 91}{space 3} .6464936
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.231503{col 50}{space 2} .3397895{col 61}{space 1}    0.75{col 70}{space 3}0.451{col 78}{space 4} .7165767{col 91}{space 3} 2.116453
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}  3.19873{col 50}{space 2} 1.102565{col 61}{space 1}    3.37{col 70}{space 3}0.001{col 78}{space 4} 1.626245{col 91}{space 3}  6.29172
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.799199{col 50}{space 2} .6013717{col 61}{space 1}    1.76{col 70}{space 3}0.079{col 78}{space 4} .9336613{col 91}{space 3}  3.46712
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}  7.46925{col 50}{space 2} 2.924152{col 61}{space 1}    5.14{col 70}{space 3}0.000{col 78}{space 4} 3.464117{col 91}{space 3} 16.10503
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 1.189334{col 50}{space 2} .4885172{col 61}{space 1}    0.42{col 70}{space 3}0.673{col 78}{space 4} .5311367{col 91}{space 3} 2.663185
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit immigration_directionworse i.equivincomeHHquintile_HBAI_imp $controls_demographics $controls_objective if infullmodel_TableD4 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       907
{txt}{col 1}Number of PSUs{col 20}= {res}      907{txt}{col 49}Population size{col 67}={res} 1,006.7824
{txt}{col 49}Design df{col 67}= {res}       906
{txt}{col 49}F({res}  23{txt},{res}    884{txt}){col 67}= {res}      6.72
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}          immigration_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}  1.51432{col 50}{space 2} .5844268{col 61}{space 1}    1.08{col 70}{space 3}0.283{col 78}{space 4} .7100206{col 91}{space 3} 3.229716
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.333983{col 50}{space 2}  .476722{col 61}{space 1}    0.81{col 70}{space 3}0.420{col 78}{space 4}  .661533{col 91}{space 3} 2.689979
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.908907{col 50}{space 2}  .665307{col 61}{space 1}    1.86{col 70}{space 3}0.064{col 78}{space 4} .9632094{col 91}{space 3}  3.78311
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.494628{col 50}{space 2} .5094426{col 61}{space 1}    1.18{col 70}{space 3}0.239{col 78}{space 4} .7656208{col 91}{space 3}  2.91778
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 2.559466{col 50}{space 2} .6820355{col 61}{space 1}    3.53{col 70}{space 3}0.000{col 78}{space 4} 1.517125{col 91}{space 3} 4.317947
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 7.746819{col 50}{space 2} 2.422385{col 61}{space 1}    6.55{col 70}{space 3}0.000{col 78}{space 4} 4.193741{col 91}{space 3} 14.31018
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .8649187{col 50}{space 2} .1869625{col 61}{space 1}   -0.67{col 70}{space 3}0.502{col 78}{space 4} .5658897{col 91}{space 3} 1.321962
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  .516291{col 50}{space 2} .1088358{col 61}{space 1}   -3.14{col 70}{space 3}0.002{col 78}{space 4} .3413647{col 91}{space 3} .7808553
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} 1.032428{col 50}{space 2} .2875799{col 61}{space 1}    0.11{col 70}{space 3}0.909{col 78}{space 4} .5976447{col 91}{space 3} 1.783515
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .5824175{col 50}{space 2}  .127716{col 61}{space 1}   -2.47{col 70}{space 3}0.014{col 78}{space 4} .3787287{col 91}{space 3} .8956549
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .3381561{col 50}{space 2}  .175906{col 61}{space 1}   -2.08{col 70}{space 3}0.037{col 78}{space 4} .1218251{col 91}{space 3} .9386368
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .3337968{col 50}{space 2} .1166511{col 61}{space 1}   -3.14{col 70}{space 3}0.002{col 78}{space 4} .1681191{col 91}{space 3} .6627462
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .1477794{col 50}{space 2} .1025179{col 61}{space 1}   -2.76{col 70}{space 3}0.006{col 78}{space 4} .0378727{col 91}{space 3} .5766358
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.441223{col 50}{space 2} .4333198{col 61}{space 1}    1.22{col 70}{space 3}0.224{col 78}{space 4} .7988503{col 91}{space 3}  2.60014
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 2.484735{col 50}{space 2} 1.243821{col 61}{space 1}    1.82{col 70}{space 3}0.069{col 78}{space 4}  .930275{col 91}{space 3} 6.636646
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.433121{col 50}{space 2} .5973314{col 61}{space 1}    0.86{col 70}{space 3}0.388{col 78}{space 4} .6324459{col 91}{space 3} 3.247448
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 5.020165{col 50}{space 2} 2.570767{col 61}{space 1}    3.15{col 70}{space 3}0.002{col 78}{space 4} 1.837574{col 91}{space 3} 13.71486
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .6914669{col 50}{space 2} .1766033{col 61}{space 1}   -1.44{col 70}{space 3}0.149{col 78}{space 4} .4188712{col 91}{space 3} 1.141464
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.845413{col 50}{space 2} .9589806{col 61}{space 1}    1.18{col 70}{space 3}0.239{col 78}{space 4} .6655332{col 91}{space 3} 5.117026
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.537274{col 50}{space 2} .7708171{col 61}{space 1}    0.86{col 70}{space 3}0.391{col 78}{space 4}  .574609{col 91}{space 3} 4.112729
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2}  1.39818{col 50}{space 2} .8245047{col 61}{space 1}    0.57{col 70}{space 3}0.570{col 78}{space 4} .4394799{col 91}{space 3} 4.448226
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.470139{col 50}{space 2} .4711989{col 61}{space 1}    1.20{col 70}{space 3}0.230{col 78}{space 4} .7837406{col 91}{space 3} 2.757685
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} .5774212{col 50}{space 2} .1611051{col 61}{space 1}   -1.97{col 70}{space 3}0.049{col 78}{space 4} .3339512{col 91}{space 3} .9983952
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 1.180449{col 50}{space 2} .6219186{col 61}{space 1}    0.31{col 70}{space 3}0.753{col 78}{space 4}  .419752{col 91}{space 3} 3.319724
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. ******* Employment Status ********
. 
. 
. * Model 1: Worsen 
. svy: logit immigration_worsen ib2.employmentstatus5 if infullmodel_TableD4 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   1{txt},{res}   3962{txt}){col 67}= {res}      0.08
{txt}{col 49}Prob > F{col 67}= {res}    0.7784

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}  Linearized
{col 1}immigration_worsen{col 20}{c |} Odds Ratio{col 32}   Std. Err.{col 44}      t{col 52}   P>|t|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}employmentstatus5 {c |}
{space 8}FT Worker  {c |}{col 20}{res}{space 2} .9712255{col 32}{space 2} .1007652{col 43}{space 1}   -0.28{col 52}{space 3}0.778{col 60}{space 4} .7924657{col 73}{space 3} 1.190309
{txt}{space 13}_cons {c |}{col 20}{res}{space 2} .2460996{col 32}{space 2} .0218975{col 43}{space 1}  -15.76{col 52}{space 3}0.000{col 60}{space 4} .2067043{col 73}{space 3} .2930033
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit immigration_worsen ib2.employmentstatus5 $controls_demographics if infullmodel_TableD4 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      7.86
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                  immigration_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} .9595341{col 50}{space 2} .1160922{col 61}{space 1}   -0.34{col 70}{space 3}0.733{col 78}{space 4} .7569091{col 91}{space 3} 1.216402
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.492692{col 50}{space 2} .2349607{col 61}{space 1}    2.54{col 70}{space 3}0.011{col 78}{space 4} 1.096336{col 91}{space 3}  2.03234
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.610056{col 50}{space 2} .2627392{col 61}{space 1}    2.92{col 70}{space 3}0.004{col 78}{space 4} 1.169214{col 91}{space 3} 2.217113
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .5958004{col 50}{space 2} .0580761{col 61}{space 1}   -5.31{col 70}{space 3}0.000{col 78}{space 4} .4921573{col 91}{space 3} .7212696
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7248211{col 50}{space 2}  .076892{col 61}{space 1}   -3.03{col 70}{space 3}0.002{col 78}{space 4} .5887139{col 91}{space 3} .8923955
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .8324747{col 50}{space 2} .0880657{col 61}{space 1}   -1.73{col 70}{space 3}0.083{col 78}{space 4} .6765451{col 91}{space 3} 1.024343
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .5974396{col 50}{space 2} .1563604{col 61}{space 1}   -1.97{col 70}{space 3}0.049{col 78}{space 4} .3576446{col 91}{space 3} .9980136
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .7180797{col 50}{space 2}  .123263{col 61}{space 1}   -1.93{col 70}{space 3}0.054{col 78}{space 4} .5128777{col 91}{space 3} 1.005383
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5878524{col 50}{space 2} .1865065{col 61}{space 1}   -1.67{col 70}{space 3}0.094{col 78}{space 4} .3155928{col 91}{space 3} 1.094988
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .6709222{col 50}{space 2} .1149382{col 61}{space 1}   -2.33{col 70}{space 3}0.020{col 78}{space 4} .4795182{col 91}{space 3} .9387268
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .7103922{col 50}{space 2} .1180463{col 61}{space 1}   -2.06{col 70}{space 3}0.040{col 78}{space 4} .5128735{col 91}{space 3} .9839796
{txt}{space 34}4  {c |}{col 38}{res}{space 2}  .780437{col 50}{space 2} .1288428{col 61}{space 1}   -1.50{col 70}{space 3}0.133{col 78}{space 4} .5646373{col 91}{space 3} 1.078713
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .5720486{col 50}{space 2}  .100061{col 61}{space 1}   -3.19{col 70}{space 3}0.001{col 78}{space 4} .4059736{col 91}{space 3} .8060612
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .8561006{col 50}{space 2} .1220303{col 61}{space 1}   -1.09{col 70}{space 3}0.276{col 78}{space 4} .6473746{col 91}{space 3} 1.132124
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.278422{col 50}{space 2} .1836731{col 61}{space 1}    1.71{col 70}{space 3}0.087{col 78}{space 4} .9645912{col 91}{space 3} 1.694358
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.037038{col 50}{space 2} .1644867{col 61}{space 1}    0.23{col 70}{space 3}0.819{col 78}{space 4} .7598757{col 91}{space 3} 1.415294
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}   1.7023{col 50}{space 2} .2648454{col 61}{space 1}    3.42{col 70}{space 3}0.001{col 78}{space 4} 1.254772{col 91}{space 3} 2.309443
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .3497026{col 50}{space 2} .0873576{col 61}{space 1}   -4.21{col 70}{space 3}0.000{col 78}{space 4} .2142886{col 91}{space 3} .5706878
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit immigration_worsen ib2.employmentstatus5 $controls_demographics $controls_objective if infullmodel_TableD4 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      6.22
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                  immigration_worsen{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} .9818258{col 50}{space 2} .1207248{col 61}{space 1}   -0.15{col 70}{space 3}0.881{col 78}{space 4} .7715056{col 91}{space 3} 1.249481
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 1.494482{col 50}{space 2} .2352497{col 61}{space 1}    2.55{col 70}{space 3}0.011{col 78}{space 4} 1.097641{col 91}{space 3} 2.034797
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 1.610733{col 50}{space 2} .2628841{col 61}{space 1}    2.92{col 70}{space 3}0.004{col 78}{space 4} 1.169657{col 91}{space 3} 2.218139
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}  .599543{col 50}{space 2} .0587938{col 61}{space 1}   -5.22{col 70}{space 3}0.000{col 78}{space 4} .4946776{col 91}{space 3} .7266385
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .7254971{col 50}{space 2} .0774474{col 61}{space 1}   -3.01{col 70}{space 3}0.003{col 78}{space 4} .5884933{col 91}{space 3} .8943961
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2}  .822743{col 50}{space 2} .0896053{col 61}{space 1}   -1.79{col 70}{space 3}0.073{col 78}{space 4} .6645552{col 91}{space 3} 1.018585
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .6033868{col 50}{space 2} .1581631{col 61}{space 1}   -1.93{col 70}{space 3}0.054{col 78}{space 4} .3609159{col 91}{space 3} 1.008755
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .7067927{col 50}{space 2} .1216358{col 61}{space 1}   -2.02{col 70}{space 3}0.044{col 78}{space 4} .5043818{col 91}{space 3} .9904322
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .5794473{col 50}{space 2} .1826008{col 61}{space 1}   -1.73{col 70}{space 3}0.083{col 78}{space 4} .3123873{col 91}{space 3} 1.074817
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .6693483{col 50}{space 2} .1154689{col 61}{space 1}   -2.33{col 70}{space 3}0.020{col 78}{space 4} .4772732{col 91}{space 3} .9387227
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .7129928{col 50}{space 2} .1187527{col 61}{space 1}   -2.03{col 70}{space 3}0.042{col 78}{space 4} .5143631{col 91}{space 3} .9883268
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .7970378{col 50}{space 2} .1319035{col 61}{space 1}   -1.37{col 70}{space 3}0.171{col 78}{space 4}  .576194{col 91}{space 3} 1.102527
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .5898411{col 50}{space 2} .1036875{col 61}{space 1}   -3.00{col 70}{space 3}0.003{col 78}{space 4} .4178858{col 91}{space 3} .8325541
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .8419726{col 50}{space 2} .1299202{col 61}{space 1}   -1.11{col 70}{space 3}0.265{col 78}{space 4} .6221765{col 91}{space 3} 1.139416
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 1.219233{col 50}{space 2} .2865309{col 61}{space 1}    0.84{col 70}{space 3}0.399{col 78}{space 4}  .769105{col 91}{space 3} 1.932805
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .8823133{col 50}{space 2} .1578064{col 61}{space 1}   -0.70{col 70}{space 3}0.484{col 78}{space 4} .6213473{col 91}{space 3} 1.252885
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 1.365186{col 50}{space 2} .2966848{col 61}{space 1}    1.43{col 70}{space 3}0.152{col 78}{space 4} .8915596{col 91}{space 3} 2.090417
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .7326398{col 50}{space 2}   .08772{col 61}{space 1}   -2.60{col 70}{space 3}0.009{col 78}{space 4} .5793538{col 91}{space 3} .9264825
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.905536{col 50}{space 2} .4550764{col 61}{space 1}    2.70{col 70}{space 3}0.007{col 78}{space 4} 1.193089{col 91}{space 3} 3.043418
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2}  1.46295{col 50}{space 2} .3511115{col 61}{space 1}    1.59{col 70}{space 3}0.113{col 78}{space 4} .9138537{col 91}{space 3} 2.341975
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .8878147{col 50}{space 2} .2416446{col 61}{space 1}   -0.44{col 70}{space 3}0.662{col 78}{space 4} .5206817{col 91}{space 3} 1.513814
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.083534{col 50}{space 2} .1647442{col 61}{space 1}    0.53{col 70}{space 3}0.598{col 78}{space 4} .8042362{col 91}{space 3} 1.459828
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} .7759497{col 50}{space 2} .1117268{col 61}{space 1}   -1.76{col 70}{space 3}0.078{col 78}{space 4} .5851052{col 91}{space 3} 1.029042
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .3013844{col 50}{space 2} .0898661{col 61}{space 1}   -4.02{col 70}{space 3}0.000{col 78}{space 4} .1679709{col 91}{space 3} .5407638
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 2: Improve 
. svy: logit immigration_improve ib2.employmentstatus5 if infullmodel_TableD4 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}   1{txt},{res}   3962{txt}){col 67}= {res}      0.75
{txt}{col 49}Prob > F{col 67}= {res}    0.3853

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}  Linearized
{col 1}immigration_improve{col 21}{c |} Odds Ratio{col 33}   Std. Err.{col 45}      t{col 53}   P>|t|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}employmentstatus5 {c |}
{space 9}FT Worker  {c |}{col 21}{res}{space 2} 1.188072{col 33}{space 2} .2358019{col 44}{space 1}    0.87{col 53}{space 3}0.385{col 61}{space 4} .8050987{col 74}{space 3} 1.753218
{txt}{space 14}_cons {c |}{col 21}{res}{space 2} .0483816{col 33}{space 2} .0085715{col 44}{space 1}  -17.10{col 53}{space 3}0.000{col 61}{space 4} .0341847{col 74}{space 3} .0684745
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit immigration_improve ib2.employmentstatus5 $controls_demographics if infullmodel_TableD4 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  17{txt},{res}   3946{txt}){col 67}= {res}      8.18
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                 immigration_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} 1.148604{col 50}{space 2} .2741665{col 61}{space 1}    0.58{col 70}{space 3}0.562{col 78}{space 4} .7193334{col 91}{space 3} 1.834046
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .5238864{col 50}{space 2} .1042154{col 61}{space 1}   -3.25{col 70}{space 3}0.001{col 78}{space 4} .3546975{col 91}{space 3} .7737776
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .2096789{col 50}{space 2}  .055851{col 61}{space 1}   -5.86{col 70}{space 3}0.000{col 78}{space 4} .1243815{col 91}{space 3} .3534709
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6261418{col 50}{space 2} .1042278{col 61}{space 1}   -2.81{col 70}{space 3}0.005{col 78}{space 4} .4517915{col 91}{space 3} .8677754
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} 1.582045{col 50}{space 2} .2810231{col 61}{space 1}    2.58{col 70}{space 3}0.010{col 78}{space 4} 1.116789{col 91}{space 3} 2.241126
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.493342{col 50}{space 2} .2813514{col 61}{space 1}    2.13{col 70}{space 3}0.033{col 78}{space 4} 1.032144{col 91}{space 3} 2.160617
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} 1.580833{col 50}{space 2} .5289845{col 61}{space 1}    1.37{col 70}{space 3}0.171{col 78}{space 4} .8202885{col 91}{space 3}  3.04653
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 2.312422{col 50}{space 2} .6619176{col 61}{space 1}    2.93{col 70}{space 3}0.003{col 78}{space 4} 1.319292{col 91}{space 3} 4.053154
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2}  2.78285{col 50}{space 2} 1.382005{col 61}{space 1}    2.06{col 70}{space 3}0.039{col 78}{space 4} 1.051093{col 91}{space 3}  7.36781
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .3807654{col 50}{space 2} .1290625{col 61}{space 1}   -2.85{col 70}{space 3}0.004{col 78}{space 4}  .195907{col 91}{space 3} .7400566
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .3380461{col 50}{space 2} .0996209{col 61}{space 1}   -3.68{col 70}{space 3}0.000{col 78}{space 4} .1896939{col 91}{space 3} .6024189
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .2992312{col 50}{space 2} .0888934{col 61}{space 1}   -4.06{col 70}{space 3}0.000{col 78}{space 4} .1671325{col 91}{space 3} .5357385
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .3015157{col 50}{space 2} .0876506{col 61}{space 1}   -4.12{col 70}{space 3}0.000{col 78}{space 4} .1705257{col 91}{space 3}  .533126
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}  .677761{col 50}{space 2} .1575544{col 61}{space 1}   -1.67{col 70}{space 3}0.094{col 78}{space 4}  .429678{col 91}{space 3}  1.06908
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} .4170093{col 50}{space 2} .1227577{col 61}{space 1}   -2.97{col 70}{space 3}0.003{col 78}{space 4} .2341507{col 91}{space 3} .7426702
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .5801413{col 50}{space 2} .1648195{col 61}{space 1}   -1.92{col 70}{space 3}0.055{col 78}{space 4} .3323774{col 91}{space 3} 1.012596
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}  .192826{col 50}{space 2} .0788054{col 61}{space 1}   -4.03{col 70}{space 3}0.000{col 78}{space 4} .0865335{col 91}{space 3} .4296815
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2}  .286955{col 50}{space 2} .1006679{col 61}{space 1}   -3.56{col 70}{space 3}0.000{col 78}{space 4} .1442477{col 91}{space 3} .5708454
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit immigration_improve ib2.employmentstatus5 $controls_demographics $controls_objective if infullmodel_TableD4 == 1, or //*M2
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,963
{txt}{col 1}Number of PSUs{col 20}= {res}    3,963{txt}{col 49}Population size{col 67}={res} 4,086.3061
{txt}{col 49}Design df{col 67}= {res}     3,962
{txt}{col 49}F({res}  23{txt},{res}   3940{txt}){col 67}= {res}      6.65
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}                 immigration_improve{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} 1.142817{col 50}{space 2} .2778781{col 61}{space 1}    0.55{col 70}{space 3}0.583{col 78}{space 4} .7094837{col 91}{space 3}  1.84082
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} .5450086{col 50}{space 2} .1092392{col 61}{space 1}   -3.03{col 70}{space 3}0.002{col 78}{space 4} .3679086{col 91}{space 3} .8073591
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} .2160861{col 50}{space 2} .0575362{col 61}{space 1}   -5.75{col 70}{space 3}0.000{col 78}{space 4} .1282072{col 91}{space 3} .3642011
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .6327077{col 50}{space 2} .1066214{col 61}{space 1}   -2.72{col 70}{space 3}0.007{col 78}{space 4} .4546929{col 91}{space 3} .8804165
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  1.59962{col 50}{space 2} .2886746{col 61}{space 1}    2.60{col 70}{space 3}0.009{col 78}{space 4} 1.122945{col 91}{space 3} 2.278639
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} 1.530704{col 50}{space 2} .2892899{col 61}{space 1}    2.25{col 70}{space 3}0.024{col 78}{space 4}  1.05675{col 91}{space 3} 2.217227
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}   1.5984{col 50}{space 2} .5418812{col 61}{space 1}    1.38{col 70}{space 3}0.167{col 78}{space 4} .8222948{col 91}{space 3} 3.107017
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} 2.379369{col 50}{space 2} .6929929{col 61}{space 1}    2.98{col 70}{space 3}0.003{col 78}{space 4} 1.344228{col 91}{space 3} 4.211634
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} 2.843035{col 50}{space 2} 1.420095{col 61}{space 1}    2.09{col 70}{space 3}0.037{col 78}{space 4} 1.067769{col 91}{space 3} 7.569848
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .3855199{col 50}{space 2} .1295655{col 61}{space 1}   -2.84{col 70}{space 3}0.005{col 78}{space 4} .1994746{col 91}{space 3} .7450853
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .3391026{col 50}{space 2}   .10098{col 61}{space 1}   -3.63{col 70}{space 3}0.000{col 78}{space 4} .1891376{col 91}{space 3} .6079734
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .2984927{col 50}{space 2} .0892649{col 61}{space 1}   -4.04{col 70}{space 3}0.000{col 78}{space 4} .1660742{col 91}{space 3} .5364944
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .2989838{col 50}{space 2} .0867787{col 61}{space 1}   -4.16{col 70}{space 3}0.000{col 78}{space 4} .1692446{col 91}{space 3} .5281783
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .6383978{col 50}{space 2}  .155782{col 61}{space 1}   -1.84{col 70}{space 3}0.066{col 78}{space 4} .3956557{col 91}{space 3} 1.030067
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} .5915131{col 50}{space 2} .2114711{col 61}{space 1}   -1.47{col 70}{space 3}0.142{col 78}{space 4} .2934671{col 91}{space 3} 1.192256
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} .6668142{col 50}{space 2} .2494675{col 61}{space 1}   -1.08{col 70}{space 3}0.279{col 78}{space 4} .3202265{col 91}{space 3} 1.388521
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .2398477{col 50}{space 2} .1187657{col 61}{space 1}   -2.88{col 70}{space 3}0.004{col 78}{space 4} .0908481{col 91}{space 3} .6332204
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} 1.153029{col 50}{space 2} .2352344{col 61}{space 1}    0.70{col 70}{space 3}0.485{col 78}{space 4}  .772912{col 91}{space 3} 1.720088
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} .9666115{col 50}{space 2} .4463419{col 61}{space 1}   -0.07{col 70}{space 3}0.941{col 78}{space 4} .3909143{col 91}{space 3} 2.390134
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} .9426562{col 50}{space 2} .4153522{col 61}{space 1}   -0.13{col 70}{space 3}0.893{col 78}{space 4} .3973591{col 91}{space 3} 2.236266
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .5014466{col 50}{space 2} .2101865{col 61}{space 1}   -1.65{col 70}{space 3}0.100{col 78}{space 4}  .220458{col 91}{space 3} 1.140574
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .6785262{col 50}{space 2} .1817789{col 61}{space 1}   -1.45{col 70}{space 3}0.148{col 78}{space 4} .4012896{col 91}{space 3} 1.147296
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} 1.120495{col 50}{space 2} .2439613{col 61}{space 1}    0.52{col 70}{space 3}0.601{col 78}{space 4} .7311801{col 91}{space 3}   1.7171
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2591063{col 50}{space 2} .1204827{col 61}{space 1}   -2.90{col 70}{space 3}0.004{col 78}{space 4} .1041247{col 91}{space 3} .6447659
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * Model 3: Worsen v Improve
. svy: logit immigration_directionworse ib2.employmentstatus5 if infullmodel_TableD4 == 1, or //* Bivariate
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       907
{txt}{col 1}Number of PSUs{col 20}= {res}      907{txt}{col 49}Population size{col 67}={res} 1,006.7824
{txt}{col 49}Design df{col 67}= {res}       906
{txt}{col 49}F({res}   1{txt},{res}    906{txt}){col 67}= {res}      0.75
{txt}{col 49}Prob > F{col 67}= {res}    0.3862

{txt}{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}  Linearized
{col 1}immigration_directionworse{col 28}{c |} Odds Ratio{col 40}   Std. Err.{col 52}      t{col 60}   P>|t|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}employmentstatus5 {c |}
{space 16}FT Worker  {c |}{col 28}{res}{space 2} .8292885{col 40}{space 2}   .17906{col 51}{space 1}   -0.87{col 60}{space 3}0.386{col 68}{space 4} .5428356{col 81}{space 3} 1.266902
{txt}{space 21}_cons {c |}{col 28}{res}{space 2}  4.27954{col 40}{space 2}  .819524{col 51}{space 1}    7.59{col 60}{space 3}0.000{col 68}{space 4} 2.938837{col 81}{space 3} 6.231874
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit immigration_directionworse ib2.employmentstatus5 $controls_demographics if infullmodel_TableD4 == 1, or //* M1
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       907
{txt}{col 1}Number of PSUs{col 20}= {res}      907{txt}{col 49}Population size{col 67}={res} 1,006.7824
{txt}{col 49}Design df{col 67}= {res}       906
{txt}{col 49}F({res}  17{txt},{res}    890{txt}){col 67}= {res}      8.38
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}          immigration_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} .9822103{col 50}{space 2} .2599657{col 61}{space 1}   -0.07{col 70}{space 3}0.946{col 78}{space 4}  .584268{col 91}{space 3} 1.651189
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 2.527205{col 50}{space 2} .6544328{col 61}{space 1}    3.58{col 70}{space 3}0.000{col 78}{space 4} 1.520277{col 91}{space 3} 4.201055
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 7.166644{col 50}{space 2} 2.219853{col 61}{space 1}    6.36{col 70}{space 3}0.000{col 78}{space 4} 3.902162{col 91}{space 3} 13.16214
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .8566872{col 50}{space 2} .1800572{col 61}{space 1}   -0.74{col 70}{space 3}0.462{col 78}{space 4} .5671249{col 91}{space 3} 1.294094
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .5189287{col 50}{space 2} .1065262{col 61}{space 1}   -3.20{col 70}{space 3}0.001{col 78}{space 4} .3468474{col 91}{space 3} .7763846
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .6242586{col 50}{space 2}   .13483{col 61}{space 1}   -2.18{col 70}{space 3}0.029{col 78}{space 4} .4085756{col 91}{space 3} .9537984
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}  .373052{col 50}{space 2} .1877147{col 61}{space 1}   -1.96{col 70}{space 3}0.050{col 78}{space 4}  .138958{col 91}{space 3}  1.00151
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .3478355{col 50}{space 2} .1153555{col 61}{space 1}   -3.18{col 70}{space 3}0.002{col 78}{space 4} .1814284{col 91}{space 3}  .666872
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .1620861{col 50}{space 2} .1142552{col 61}{space 1}   -2.58{col 70}{space 3}0.010{col 78}{space 4} .0406376{col 91}{space 3} .6464936
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} 1.480791{col 50}{space 2} .5637549{col 61}{space 1}    1.03{col 70}{space 3}0.303{col 78}{space 4} .7014516{col 91}{space 3} 3.126007
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.469768{col 50}{space 2} .5058497{col 61}{space 1}    1.12{col 70}{space 3}0.263{col 78}{space 4} .7479959{col 91}{space 3} 2.888009
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.895567{col 50}{space 2} .6429994{col 61}{space 1}    1.89{col 70}{space 3}0.060{col 78}{space 4} .9741258{col 91}{space 3} 3.688615
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.503835{col 50}{space 2}  .508504{col 61}{space 1}    1.21{col 70}{space 3}0.228{col 78}{space 4} .7744465{col 91}{space 3} 2.920175
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.231503{col 50}{space 2} .3397895{col 61}{space 1}    0.75{col 70}{space 3}0.451{col 78}{space 4} .7165767{col 91}{space 3} 2.116453
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}  3.19873{col 50}{space 2} 1.102565{col 61}{space 1}    3.37{col 70}{space 3}0.001{col 78}{space 4} 1.626245{col 91}{space 3}  6.29172
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.799199{col 50}{space 2} .6013717{col 61}{space 1}    1.76{col 70}{space 3}0.079{col 78}{space 4} .9336613{col 91}{space 3}  3.46712
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}  7.46925{col 50}{space 2} 2.924152{col 61}{space 1}    5.14{col 70}{space 3}0.000{col 78}{space 4} 3.464117{col 91}{space 3} 16.10503
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 1.210875{col 50}{space 2} .5013071{col 61}{space 1}    0.46{col 70}{space 3}0.644{col 78}{space 4} .5373127{col 91}{space 3} 2.728799
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. svy: logit immigration_directionworse ib2.employmentstatus5 $controls_demographics $controls_objective if infullmodel_TableD4 == 1, or //*M2    
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       907
{txt}{col 1}Number of PSUs{col 20}= {res}      907{txt}{col 49}Population size{col 67}={res} 1,006.7824
{txt}{col 49}Design df{col 67}= {res}       906
{txt}{col 49}F({res}  23{txt},{res}    884{txt}){col 67}= {res}      6.72
{txt}{col 49}Prob > F{col 67}= {res}    0.0000

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}          immigration_directionworse{col 38}{c |} Odds Ratio{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}employmentstatus5 {c |}
{space 26}FT Worker  {c |}{col 38}{res}{space 2} .9685902{col 50}{space 2}  .269798{col 61}{space 1}   -0.11{col 70}{space 3}0.909{col 78}{space 4} .5606905{col 91}{space 3} 1.673235
{txt}{space 36} {c |}
{space 26}age_3group {c |}
{space 30}30-49  {c |}{col 38}{res}{space 2} 2.559466{col 50}{space 2} .6820355{col 61}{space 1}    3.53{col 70}{space 3}0.000{col 78}{space 4} 1.517125{col 91}{space 3} 4.317947
{txt}{space 32}50+  {c |}{col 38}{res}{space 2} 7.746819{col 50}{space 2} 2.422385{col 61}{space 1}    6.55{col 70}{space 3}0.000{col 78}{space 4} 4.193741{col 91}{space 3} 14.31018
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .8649187{col 50}{space 2} .1869625{col 61}{space 1}   -0.67{col 70}{space 3}0.502{col 78}{space 4} .5658897{col 91}{space 3} 1.321962
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  .516291{col 50}{space 2} .1088358{col 61}{space 1}   -3.14{col 70}{space 3}0.002{col 78}{space 4} .3413647{col 91}{space 3} .7808553
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .5824175{col 50}{space 2}  .127716{col 61}{space 1}   -2.47{col 70}{space 3}0.014{col 78}{space 4} .3787287{col 91}{space 3} .8956549
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .3381561{col 50}{space 2}  .175906{col 61}{space 1}   -2.08{col 70}{space 3}0.037{col 78}{space 4} .1218251{col 91}{space 3} .9386368
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .3337968{col 50}{space 2} .1166511{col 61}{space 1}   -3.14{col 70}{space 3}0.002{col 78}{space 4} .1681191{col 91}{space 3} .6627462
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .1477794{col 50}{space 2} .1025179{col 61}{space 1}   -2.76{col 70}{space 3}0.006{col 78}{space 4} .0378727{col 91}{space 3} .5766358
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}  1.51432{col 50}{space 2} .5844268{col 61}{space 1}    1.08{col 70}{space 3}0.283{col 78}{space 4} .7100206{col 91}{space 3} 3.229716
{txt}{space 34}3  {c |}{col 38}{res}{space 2} 1.333983{col 50}{space 2}  .476722{col 61}{space 1}    0.81{col 70}{space 3}0.420{col 78}{space 4}  .661533{col 91}{space 3} 2.689979
{txt}{space 34}4  {c |}{col 38}{res}{space 2} 1.908907{col 50}{space 2}  .665307{col 61}{space 1}    1.86{col 70}{space 3}0.064{col 78}{space 4} .9632094{col 91}{space 3}  3.78311
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} 1.494628{col 50}{space 2} .5094426{col 61}{space 1}    1.18{col 70}{space 3}0.239{col 78}{space 4} .7656208{col 91}{space 3}  2.91778
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} 1.441223{col 50}{space 2} .4333198{col 61}{space 1}    1.22{col 70}{space 3}0.224{col 78}{space 4} .7988503{col 91}{space 3}  2.60014
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} 2.484735{col 50}{space 2} 1.243821{col 61}{space 1}    1.82{col 70}{space 3}0.069{col 78}{space 4}  .930275{col 91}{space 3} 6.636646
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} 1.433121{col 50}{space 2} .5973314{col 61}{space 1}    0.86{col 70}{space 3}0.388{col 78}{space 4} .6324459{col 91}{space 3} 3.247448
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} 5.020165{col 50}{space 2} 2.570767{col 61}{space 1}    3.15{col 70}{space 3}0.002{col 78}{space 4} 1.837574{col 91}{space 3} 13.71486
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .6914669{col 50}{space 2} .1766033{col 61}{space 1}   -1.44{col 70}{space 3}0.149{col 78}{space 4} .4188712{col 91}{space 3} 1.141464
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} 1.845413{col 50}{space 2} .9589806{col 61}{space 1}    1.18{col 70}{space 3}0.239{col 78}{space 4} .6655332{col 91}{space 3} 5.117026
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} 1.537274{col 50}{space 2} .7708171{col 61}{space 1}    0.86{col 70}{space 3}0.391{col 78}{space 4}  .574609{col 91}{space 3} 4.112729
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2}  1.39818{col 50}{space 2} .8245047{col 61}{space 1}    0.57{col 70}{space 3}0.570{col 78}{space 4} .4394799{col 91}{space 3} 4.448226
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} 1.470139{col 50}{space 2} .4711989{col 61}{space 1}    1.20{col 70}{space 3}0.230{col 78}{space 4} .7837406{col 91}{space 3} 2.757685
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} .5774212{col 50}{space 2} .1611051{col 61}{space 1}   -1.97{col 70}{space 3}0.049{col 78}{space 4} .3339512{col 91}{space 3} .9983952
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 1.218729{col 50}{space 2} .6602167{col 61}{space 1}    0.37{col 70}{space 3}0.715{col 78}{space 4} .4208942{col 91}{space 3} 3.528918
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}.         
.         
.         
.         
. ************************************************************************************************************
. 
. * APPENDIX E: Full Models for Figure 2
. 
. 
. * Set Control Variables  
. global controls_objective aiexposure_felten2021 i.aiexposure_pizzinelli2023 i.aiexposure_gmyrek2023  
{txt}
{com}. global controls_demographics age i.female_dummy i.degree_dummy i.employmentstatus5 i.employsector5 i.equivincomeHHquintile_HBAI_imp i.isco08_5group
{txt}
{com}. global controls_politics  i.pid2024 i.profile_leftrightplacement         
{txt}
{com}. 
. *Set Sample Size
. svy: reg polpreferences1_redistribution i.jobimpact2_AI $controls_demographics $controls_politics $controls_objective
{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,633
{txt}{col 1}Number of PSUs{col 20}= {res}    3,633{txt}{col 49}Population size{col 67}={res}  3,737.245
{txt}{col 49}Design df{col 67}= {res}     3,632
{txt}{col 49}F({res}  39{txt},{res}   3594{txt}){col 67}= {res}     36.62
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.2608

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}      polpreferences1_redistribution{col 38}{c |}      Coef.{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}jobimpact2_AI {c |}
{space 29}Worsen  {c |}{col 38}{res}{space 2} .1055692{col 50}{space 2} .0505023{col 61}{space 1}    2.09{col 70}{space 3}0.037{col 78}{space 4} .0065535{col 91}{space 3}  .204585
{txt}{space 28}Improve  {c |}{col 38}{res}{space 2} .0948219{col 50}{space 2} .0735572{col 61}{space 1}    1.29{col 70}{space 3}0.197{col 78}{space 4}-.0493956{col 91}{space 3} .2390393
{txt}{space 25}Don't Know  {c |}{col 38}{res}{space 2}  .013855{col 50}{space 2} .0590388{col 61}{space 1}    0.23{col 70}{space 3}0.814{col 78}{space 4}-.1018975{col 91}{space 3} .1296075
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2}  .001282{col 50}{space 2} .0018224{col 61}{space 1}    0.70{col 70}{space 3}0.482{col 78}{space 4}-.0022911{col 91}{space 3} .0048551
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}-.1103217{col 50}{space 2} .0442056{col 61}{space 1}   -2.50{col 70}{space 3}0.013{col 78}{space 4}-.1969921{col 91}{space 3}-.0236514
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .0462259{col 50}{space 2} .0457156{col 61}{space 1}    1.01{col 70}{space 3}0.312{col 78}{space 4}-.0434049{col 91}{space 3} .1358566
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} -.022575{col 50}{space 2} .0518905{col 61}{space 1}   -0.44{col 70}{space 3}0.664{col 78}{space 4}-.1243125{col 91}{space 3} .0791625
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .0258314{col 50}{space 2} .0479135{col 61}{space 1}    0.54{col 70}{space 3}0.590{col 78}{space 4}-.0681087{col 91}{space 3} .1197715
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .1531819{col 50}{space 2}  .089119{col 61}{space 1}    1.72{col 70}{space 3}0.086{col 78}{space 4}-.0215463{col 91}{space 3} .3279101
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2}-.1683704{col 50}{space 2} .0738443{col 61}{space 1}   -2.28{col 70}{space 3}0.023{col 78}{space 4}-.3131509{col 91}{space 3}-.0235899
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .2221254{col 50}{space 2} .1209937{col 61}{space 1}    1.84{col 70}{space 3}0.066{col 78}{space 4}-.0150969{col 91}{space 3} .4593477
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}-.0377159{col 50}{space 2} .0809142{col 61}{space 1}   -0.47{col 70}{space 3}0.641{col 78}{space 4}-.1963577{col 91}{space 3} .1209259
{txt}{space 34}3  {c |}{col 38}{res}{space 2} -.063061{col 50}{space 2} .0823252{col 61}{space 1}   -0.77{col 70}{space 3}0.444{col 78}{space 4}-.2244693{col 91}{space 3} .0983473
{txt}{space 34}4  {c |}{col 38}{res}{space 2}-.2810713{col 50}{space 2} .0815204{col 61}{space 1}   -3.45{col 70}{space 3}0.001{col 78}{space 4}-.4409015{col 91}{space 3} -.121241
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2}-.4132883{col 50}{space 2} .0827212{col 61}{space 1}   -5.00{col 70}{space 3}0.000{col 78}{space 4}-.5754729{col 91}{space 3}-.2511037
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}-.0155373{col 50}{space 2} .0627718{col 61}{space 1}   -0.25{col 70}{space 3}0.805{col 78}{space 4}-.1386088{col 91}{space 3} .1075342
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}-.2636036{col 50}{space 2} .1093962{col 61}{space 1}   -2.41{col 70}{space 3}0.016{col 78}{space 4}-.4780876{col 91}{space 3}-.0491196
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2}-.1081665{col 50}{space 2} .0776321{col 61}{space 1}   -1.39{col 70}{space 3}0.164{col 78}{space 4}-.2603732{col 91}{space 3} .0440403
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}-.0011309{col 50}{space 2} .0995273{col 61}{space 1}   -0.01{col 70}{space 3}0.991{col 78}{space 4}-.1962658{col 91}{space 3} .1940039
{txt}{space 36} {c |}
{space 29}pid2024 {c |}
{space 29}Labour  {c |}{col 38}{res}{space 2} .7377595{col 50}{space 2} .0817099{col 61}{space 1}    9.03{col 70}{space 3}0.000{col 78}{space 4} .5775577{col 91}{space 3} .8979613
{txt}{space 28}Liberal  {c |}{col 38}{res}{space 2} .7153253{col 50}{space 2} .1036983{col 61}{space 1}    6.90{col 70}{space 3}0.000{col 78}{space 4} .5120125{col 91}{space 3}  .918638
{txt}{space 30}Green  {c |}{col 38}{res}{space 2} .6940048{col 50}{space 2} .1058397{col 61}{space 1}    6.56{col 70}{space 3}0.000{col 78}{space 4} .4864936{col 91}{space 3}  .901516
{txt}{space 29}Reform  {c |}{col 38}{res}{space 2} .1602423{col 50}{space 2} .1324792{col 61}{space 1}    1.21{col 70}{space 3}0.227{col 78}{space 4}-.0994987{col 91}{space 3} .4199833
{txt}{space 30}Other  {c |}{col 38}{res}{space 2} .6586456{col 50}{space 2} .1186378{col 61}{space 1}    5.55{col 70}{space 3}0.000{col 78}{space 4} .4260422{col 91}{space 3}  .891249
{txt}{space 25}Don't Know  {c |}{col 38}{res}{space 2} .4363632{col 50}{space 2} .1049672{col 61}{space 1}    4.16{col 70}{space 3}0.000{col 78}{space 4} .2305627{col 91}{space 3} .6421637
{txt}{space 31}None  {c |}{col 38}{res}{space 2} .5491572{col 50}{space 2} .0777115{col 61}{space 1}    7.07{col 70}{space 3}0.000{col 78}{space 4} .3967946{col 91}{space 3} .7015197
{txt}{space 36} {c |}
{space 10}profile_leftrightplacement {c |}
{space 19}Fairly left-wing  {c |}{col 38}{res}{space 2}-.3134064{col 50}{space 2} .0850098{col 61}{space 1}   -3.69{col 70}{space 3}0.000{col 78}{space 4} -.480078{col 91}{space 3}-.1467347
{txt}{space 12}Slightly left-of-centre  {c |}{col 38}{res}{space 2}-.7476248{col 50}{space 2} .0893811{col 61}{space 1}   -8.36{col 70}{space 3}0.000{col 78}{space 4} -.922867{col 91}{space 3}-.5723826
{txt}{space 29}Centre  {c |}{col 38}{res}{space 2}-1.163259{col 50}{space 2} .0934701{col 61}{space 1}  -12.45{col 70}{space 3}0.000{col 78}{space 4}-1.346518{col 91}{space 3}-.9799998
{txt}{space 11}Slightly right-of-centre  {c |}{col 38}{res}{space 2}-1.616472{col 50}{space 2} .1092492{col 61}{space 1}  -14.80{col 70}{space 3}0.000{col 78}{space 4}-1.830668{col 91}{space 3}-1.402276
{txt}{space 18}Fairly right-wing  {c |}{col 38}{res}{space 2}-1.645728{col 50}{space 2} .1327517{col 61}{space 1}  -12.40{col 70}{space 3}0.000{col 78}{space 4}-1.906003{col 91}{space 3}-1.385453
{txt}{space 20}Very right-wing  {c |}{col 38}{res}{space 2}-1.742683{col 50}{space 2} .2496626{col 61}{space 1}   -6.98{col 70}{space 3}0.000{col 78}{space 4}-2.232176{col 91}{space 3} -1.25319
{txt}{space 25}Don’t know  {c |}{col 38}{res}{space 2}-1.197418{col 50}{space 2} .0908743{col 61}{space 1}  -13.18{col 70}{space 3}0.000{col 78}{space 4}-1.375587{col 91}{space 3}-1.019248
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2}-.0170566{col 50}{space 2} .0558349{col 61}{space 1}   -0.31{col 70}{space 3}0.760{col 78}{space 4}-.1265275{col 91}{space 3} .0924142
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2}-.1320424{col 50}{space 2} .1049473{col 61}{space 1}   -1.26{col 70}{space 3}0.208{col 78}{space 4}-.3378039{col 91}{space 3} .0737192
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2}-.2061814{col 50}{space 2} .1051801{col 61}{space 1}   -1.96{col 70}{space 3}0.050{col 78}{space 4}-.4123992{col 91}{space 3} .0000365
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .2111524{col 50}{space 2} .1232427{col 61}{space 1}    1.71{col 70}{space 3}0.087{col 78}{space 4}-.0304795{col 91}{space 3} .4527842
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .0027637{col 50}{space 2} .0658365{col 61}{space 1}    0.04{col 70}{space 3}0.967{col 78}{space 4}-.1263165{col 91}{space 3} .1318438
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} .1312173{col 50}{space 2} .0589496{col 61}{space 1}    2.23{col 70}{space 3}0.026{col 78}{space 4} .0156396{col 91}{space 3} .2467949
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 4.289795{col 50}{space 2} .1772194{col 61}{space 1}   24.21{col 70}{space 3}0.000{col 78}{space 4} 3.942336{col 91}{space 3} 4.637255
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. generate infullmodel_redist1 = e(sample)
{txt}
{com}. svy: reg polpreferences6_education i.jobimpact2_AI $controls_demographics $controls_politics $controls_objective
{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,537
{txt}{col 1}Number of PSUs{col 20}= {res}    3,537{txt}{col 49}Population size{col 67}={res} 3,649.7904
{txt}{col 49}Design df{col 67}= {res}     3,536
{txt}{col 49}F({res}  39{txt},{res}   3498{txt}){col 67}= {res}     21.80
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.2027

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}           polpreferences6_education{col 38}{c |}      Coef.{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}jobimpact2_AI {c |}
{space 29}Worsen  {c |}{col 38}{res}{space 2}-.0351956{col 50}{space 2} .0502827{col 61}{space 1}   -0.70{col 70}{space 3}0.484{col 78}{space 4}-.1337816{col 91}{space 3} .0633904
{txt}{space 28}Improve  {c |}{col 38}{res}{space 2} .3349367{col 50}{space 2} .0681122{col 61}{space 1}    4.92{col 70}{space 3}0.000{col 78}{space 4} .2013934{col 91}{space 3} .4684799
{txt}{space 25}Don't Know  {c |}{col 38}{res}{space 2}-.0059064{col 50}{space 2} .0569648{col 61}{space 1}   -0.10{col 70}{space 3}0.917{col 78}{space 4}-.1175936{col 91}{space 3} .1057808
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2} .0004198{col 50}{space 2} .0018274{col 61}{space 1}    0.23{col 70}{space 3}0.818{col 78}{space 4}-.0031632{col 91}{space 3} .0040028
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}-.0933917{col 50}{space 2} .0422919{col 61}{space 1}   -2.21{col 70}{space 3}0.027{col 78}{space 4}-.1763107{col 91}{space 3}-.0104727
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .1645408{col 50}{space 2} .0443555{col 61}{space 1}    3.71{col 70}{space 3}0.000{col 78}{space 4} .0775758{col 91}{space 3} .2515058
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .1584539{col 50}{space 2} .0515919{col 61}{space 1}    3.07{col 70}{space 3}0.002{col 78}{space 4} .0573011{col 91}{space 3} .2596068
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2}  .079805{col 50}{space 2} .0472987{col 61}{space 1}    1.69{col 70}{space 3}0.092{col 78}{space 4}-.0129304{col 91}{space 3} .1725405
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}-.0011325{col 50}{space 2} .0832354{col 61}{space 1}   -0.01{col 70}{space 3}0.989{col 78}{space 4}-.1643268{col 91}{space 3} .1620618
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .1524948{col 50}{space 2} .0670412{col 61}{space 1}    2.27{col 70}{space 3}0.023{col 78}{space 4} .0210515{col 91}{space 3} .2839381
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2}  .362627{col 50}{space 2} .1586247{col 61}{space 1}    2.29{col 70}{space 3}0.022{col 78}{space 4} .0516218{col 91}{space 3} .6736322
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .0740994{col 50}{space 2} .0809831{col 61}{space 1}    0.91{col 70}{space 3}0.360{col 78}{space 4}-.0846789{col 91}{space 3} .2328778
{txt}{space 34}3  {c |}{col 38}{res}{space 2}-.0011869{col 50}{space 2} .0794739{col 61}{space 1}   -0.01{col 70}{space 3}0.988{col 78}{space 4}-.1570061{col 91}{space 3} .1546324
{txt}{space 34}4  {c |}{col 38}{res}{space 2}-.1004544{col 50}{space 2} .0767597{col 61}{space 1}   -1.31{col 70}{space 3}0.191{col 78}{space 4} -.250952{col 91}{space 3} .0500433
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2}-.1059311{col 50}{space 2} .0799563{col 61}{space 1}   -1.32{col 70}{space 3}0.185{col 78}{space 4}-.2626961{col 91}{space 3}  .050834
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}-.0367639{col 50}{space 2} .0607841{col 61}{space 1}   -0.60{col 70}{space 3}0.545{col 78}{space 4}-.1559393{col 91}{space 3} .0824114
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} .0356075{col 50}{space 2} .1044856{col 61}{space 1}    0.34{col 70}{space 3}0.733{col 78}{space 4}-.1692507{col 91}{space 3} .2404658
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2}-.0088752{col 50}{space 2} .0806831{col 61}{space 1}   -0.11{col 70}{space 3}0.912{col 78}{space 4}-.1670652{col 91}{space 3} .1493149
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}-.1260558{col 50}{space 2} .0995952{col 61}{space 1}   -1.27{col 70}{space 3}0.206{col 78}{space 4}-.3213256{col 91}{space 3}  .069214
{txt}{space 36} {c |}
{space 29}pid2024 {c |}
{space 29}Labour  {c |}{col 38}{res}{space 2}  .262007{col 50}{space 2} .0759241{col 61}{space 1}    3.45{col 70}{space 3}0.001{col 78}{space 4} .1131476{col 91}{space 3} .4108664
{txt}{space 28}Liberal  {c |}{col 38}{res}{space 2} .3194845{col 50}{space 2} .1000014{col 61}{space 1}    3.19{col 70}{space 3}0.001{col 78}{space 4} .1234183{col 91}{space 3} .5155506
{txt}{space 30}Green  {c |}{col 38}{res}{space 2} .3723776{col 50}{space 2} .1078641{col 61}{space 1}    3.45{col 70}{space 3}0.001{col 78}{space 4} .1608954{col 91}{space 3} .5838598
{txt}{space 29}Reform  {c |}{col 38}{res}{space 2}-.2166618{col 50}{space 2} .1001926{col 61}{space 1}   -2.16{col 70}{space 3}0.031{col 78}{space 4}-.4131028{col 91}{space 3}-.0202207
{txt}{space 30}Other  {c |}{col 38}{res}{space 2} .4230259{col 50}{space 2} .1350682{col 61}{space 1}    3.13{col 70}{space 3}0.002{col 78}{space 4} .1582066{col 91}{space 3} .6878453
{txt}{space 25}Don't Know  {c |}{col 38}{res}{space 2} .2794445{col 50}{space 2} .0938785{col 61}{space 1}    2.98{col 70}{space 3}0.003{col 78}{space 4}  .095383{col 91}{space 3} .4635061
{txt}{space 31}None  {c |}{col 38}{res}{space 2} .0308751{col 50}{space 2} .0672775{col 61}{space 1}    0.46{col 70}{space 3}0.646{col 78}{space 4}-.1010316{col 91}{space 3} .1627818
{txt}{space 36} {c |}
{space 10}profile_leftrightplacement {c |}
{space 19}Fairly left-wing  {c |}{col 38}{res}{space 2}-.3307315{col 50}{space 2} .1189601{col 61}{space 1}   -2.78{col 70}{space 3}0.005{col 78}{space 4}-.5639688{col 91}{space 3}-.0974941
{txt}{space 12}Slightly left-of-centre  {c |}{col 38}{res}{space 2}-.7206547{col 50}{space 2} .1221966{col 61}{space 1}   -5.90{col 70}{space 3}0.000{col 78}{space 4}-.9602377{col 91}{space 3}-.4810716
{txt}{space 29}Centre  {c |}{col 38}{res}{space 2} -.989026{col 50}{space 2} .1233284{col 61}{space 1}   -8.02{col 70}{space 3}0.000{col 78}{space 4}-1.230828{col 91}{space 3}-.7472241
{txt}{space 11}Slightly right-of-centre  {c |}{col 38}{res}{space 2}-1.249679{col 50}{space 2} .1314978{col 61}{space 1}   -9.50{col 70}{space 3}0.000{col 78}{space 4}-1.507498{col 91}{space 3}-.9918598
{txt}{space 18}Fairly right-wing  {c |}{col 38}{res}{space 2}-1.405782{col 50}{space 2} .1423871{col 61}{space 1}   -9.87{col 70}{space 3}0.000{col 78}{space 4}-1.684951{col 91}{space 3}-1.126613
{txt}{space 20}Very right-wing  {c |}{col 38}{res}{space 2}-1.518217{col 50}{space 2} .2034459{col 61}{space 1}   -7.46{col 70}{space 3}0.000{col 78}{space 4}-1.917101{col 91}{space 3}-1.119334
{txt}{space 25}Don’t know  {c |}{col 38}{res}{space 2}-1.177068{col 50}{space 2} .1222226{col 61}{space 1}   -9.63{col 70}{space 3}0.000{col 78}{space 4}-1.416702{col 91}{space 3}-.9374343
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2}  .035949{col 50}{space 2}  .052046{col 61}{space 1}    0.69{col 70}{space 3}0.490{col 78}{space 4}-.0660943{col 91}{space 3} .1379922
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} .0699452{col 50}{space 2} .1028301{col 61}{space 1}    0.68{col 70}{space 3}0.496{col 78}{space 4}-.1316671{col 91}{space 3} .2715576
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} .0203655{col 50}{space 2} .1041149{col 61}{space 1}    0.20{col 70}{space 3}0.845{col 78}{space 4}-.1837658{col 91}{space 3} .2244968
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2}-.1996798{col 50}{space 2} .1187045{col 61}{space 1}   -1.68{col 70}{space 3}0.093{col 78}{space 4}-.4324159{col 91}{space 3} .0330564
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2}-.0918086{col 50}{space 2} .0642117{col 61}{space 1}   -1.43{col 70}{space 3}0.153{col 78}{space 4}-.2177043{col 91}{space 3} .0340871
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2}-.0070072{col 50}{space 2} .0589003{col 61}{space 1}   -0.12{col 70}{space 3}0.905{col 78}{space 4}-.1224891{col 91}{space 3} .1084747
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2}  3.34574{col 50}{space 2} .1882306{col 61}{space 1}   17.77{col 70}{space 3}0.000{col 78}{space 4} 2.976689{col 91}{space 3} 3.714792
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. generate infullmodel_education1 = e(sample)
{txt}
{com}. svy: reg polpreferences4_immigration    i.jobimpact2_AI $controls_demographics $controls_politics $controls_objective
{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,645
{txt}{col 1}Number of PSUs{col 20}= {res}    3,645{txt}{col 49}Population size{col 67}={res} 3,761.5796
{txt}{col 49}Design df{col 67}= {res}     3,644
{txt}{col 49}F({res}  39{txt},{res}   3606{txt}){col 67}= {res}     57.39
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.3297

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}         polpreferences4_immigration{col 38}{c |}      Coef.{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}jobimpact2_AI {c |}
{space 29}Worsen  {c |}{col 38}{res}{space 2} -.208027{col 50}{space 2} .0493213{col 61}{space 1}   -4.22{col 70}{space 3}0.000{col 78}{space 4}-.3047271{col 91}{space 3} -.111327
{txt}{space 28}Improve  {c |}{col 38}{res}{space 2} .1552427{col 50}{space 2} .0766498{col 61}{space 1}    2.03{col 70}{space 3}0.043{col 78}{space 4}  .004962{col 91}{space 3} .3055235
{txt}{space 25}Don't Know  {c |}{col 38}{res}{space 2}-.1085006{col 50}{space 2}  .056494{col 61}{space 1}   -1.92{col 70}{space 3}0.055{col 78}{space 4}-.2192635{col 91}{space 3} .0022623
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2}-.0119631{col 50}{space 2} .0017527{col 61}{space 1}   -6.83{col 70}{space 3}0.000{col 78}{space 4}-.0153994{col 91}{space 3}-.0085267
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .0422359{col 50}{space 2} .0432439{col 61}{space 1}    0.98{col 70}{space 3}0.329{col 78}{space 4}-.0425488{col 91}{space 3} .1270207
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .3189511{col 50}{space 2}  .045408{col 61}{space 1}    7.02{col 70}{space 3}0.000{col 78}{space 4} .2299235{col 91}{space 3} .4079787
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .1355655{col 50}{space 2}  .051204{col 61}{space 1}    2.65{col 70}{space 3}0.008{col 78}{space 4} .0351742{col 91}{space 3} .2359568
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .0370549{col 50}{space 2} .0464658{col 61}{space 1}    0.80{col 70}{space 3}0.425{col 78}{space 4}-.0540466{col 91}{space 3} .1281564
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .1634418{col 50}{space 2} .0849533{col 61}{space 1}    1.92{col 70}{space 3}0.054{col 78}{space 4}-.0031189{col 91}{space 3} .3300025
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .1145333{col 50}{space 2} .0778316{col 61}{space 1}    1.47{col 70}{space 3}0.141{col 78}{space 4}-.0380645{col 91}{space 3}  .267131
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .6025441{col 50}{space 2}  .152622{col 61}{space 1}    3.95{col 70}{space 3}0.000{col 78}{space 4} .3033112{col 91}{space 3} .9017771
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .1515783{col 50}{space 2} .0833631{col 61}{space 1}    1.82{col 70}{space 3}0.069{col 78}{space 4}-.0118646{col 91}{space 3} .3150212
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .1385061{col 50}{space 2} .0779738{col 61}{space 1}    1.78{col 70}{space 3}0.076{col 78}{space 4}-.0143706{col 91}{space 3} .2913828
{txt}{space 34}4  {c |}{col 38}{res}{space 2}  .181332{col 50}{space 2}  .075445{col 61}{space 1}    2.40{col 70}{space 3}0.016{col 78}{space 4} .0334133{col 91}{space 3} .3292506
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .3170084{col 50}{space 2} .0821159{col 61}{space 1}    3.86{col 70}{space 3}0.000{col 78}{space 4} .1560107{col 91}{space 3} .4780062
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}-.1170635{col 50}{space 2} .0617218{col 61}{space 1}   -1.90{col 70}{space 3}0.058{col 78}{space 4}-.2380762{col 91}{space 3} .0039492
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}-.1795786{col 50}{space 2} .0925368{col 61}{space 1}   -1.94{col 70}{space 3}0.052{col 78}{space 4}-.3610076{col 91}{space 3} .0018504
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2}-.1540218{col 50}{space 2} .0830966{col 61}{space 1}   -1.85{col 70}{space 3}0.064{col 78}{space 4}-.3169424{col 91}{space 3} .0088987
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}-.1671382{col 50}{space 2} .1089614{col 61}{space 1}   -1.53{col 70}{space 3}0.125{col 78}{space 4}-.3807696{col 91}{space 3} .0464932
{txt}{space 36} {c |}
{space 29}pid2024 {c |}
{space 29}Labour  {c |}{col 38}{res}{space 2} .3773833{col 50}{space 2} .0782941{col 61}{space 1}    4.82{col 70}{space 3}0.000{col 78}{space 4} .2238786{col 91}{space 3} .5308879
{txt}{space 28}Liberal  {c |}{col 38}{res}{space 2} .6032731{col 50}{space 2} .1061544{col 61}{space 1}    5.68{col 70}{space 3}0.000{col 78}{space 4} .3951451{col 91}{space 3} .8114011
{txt}{space 30}Green  {c |}{col 38}{res}{space 2}  .654981{col 50}{space 2} .1056069{col 61}{space 1}    6.20{col 70}{space 3}0.000{col 78}{space 4} .4479266{col 91}{space 3} .8620354
{txt}{space 29}Reform  {c |}{col 38}{res}{space 2} -.508061{col 50}{space 2} .0830595{col 61}{space 1}   -6.12{col 70}{space 3}0.000{col 78}{space 4}-.6709088{col 91}{space 3}-.3452133
{txt}{space 30}Other  {c |}{col 38}{res}{space 2} .5267196{col 50}{space 2} .1319776{col 61}{space 1}    3.99{col 70}{space 3}0.000{col 78}{space 4} .2679624{col 91}{space 3} .7854769
{txt}{space 25}Don't Know  {c |}{col 38}{res}{space 2} .3241368{col 50}{space 2} .0963942{col 61}{space 1}    3.36{col 70}{space 3}0.001{col 78}{space 4} .1351448{col 91}{space 3} .5131288
{txt}{space 31}None  {c |}{col 38}{res}{space 2} .1821247{col 50}{space 2} .0701614{col 61}{space 1}    2.60{col 70}{space 3}0.009{col 78}{space 4} .0445652{col 91}{space 3} .3196843
{txt}{space 36} {c |}
{space 10}profile_leftrightplacement {c |}
{space 19}Fairly left-wing  {c |}{col 38}{res}{space 2}-.4442855{col 50}{space 2} .1106493{col 61}{space 1}   -4.02{col 70}{space 3}0.000{col 78}{space 4}-.6612263{col 91}{space 3}-.2273447
{txt}{space 12}Slightly left-of-centre  {c |}{col 38}{res}{space 2}-.7824472{col 50}{space 2} .1143844{col 61}{space 1}   -6.84{col 70}{space 3}0.000{col 78}{space 4}-1.006711{col 91}{space 3}-.5581833
{txt}{space 29}Centre  {c |}{col 38}{res}{space 2}-1.118947{col 50}{space 2} .1182519{col 61}{space 1}   -9.46{col 70}{space 3}0.000{col 78}{space 4}-1.350793{col 91}{space 3}-.8871006
{txt}{space 11}Slightly right-of-centre  {c |}{col 38}{res}{space 2}-1.459984{col 50}{space 2} .1257269{col 61}{space 1}  -11.61{col 70}{space 3}0.000{col 78}{space 4}-1.706486{col 91}{space 3}-1.213482
{txt}{space 18}Fairly right-wing  {c |}{col 38}{res}{space 2}-1.678568{col 50}{space 2} .1376944{col 61}{space 1}  -12.19{col 70}{space 3}0.000{col 78}{space 4}-1.948534{col 91}{space 3}-1.408603
{txt}{space 20}Very right-wing  {c |}{col 38}{res}{space 2}-1.969072{col 50}{space 2} .1715861{col 61}{space 1}  -11.48{col 70}{space 3}0.000{col 78}{space 4}-2.305487{col 91}{space 3}-1.632658
{txt}{space 25}Don’t know  {c |}{col 38}{res}{space 2}-1.306213{col 50}{space 2}  .115668{col 61}{space 1}  -11.29{col 70}{space 3}0.000{col 78}{space 4}-1.532994{col 91}{space 3}-1.079433
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .0561296{col 50}{space 2}  .054422{col 61}{space 1}    1.03{col 70}{space 3}0.302{col 78}{space 4}-.0505709{col 91}{space 3} .1628302
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2}-.0734838{col 50}{space 2} .1105749{col 61}{space 1}   -0.66{col 70}{space 3}0.506{col 78}{space 4}-.2902787{col 91}{space 3} .1433111
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2}-.0792279{col 50}{space 2} .1084728{col 61}{space 1}   -0.73{col 70}{space 3}0.465{col 78}{space 4}-.2919013{col 91}{space 3} .1334455
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .0105041{col 50}{space 2} .1066825{col 61}{space 1}    0.10{col 70}{space 3}0.922{col 78}{space 4}-.1986592{col 91}{space 3} .2196673
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2}-.0194018{col 50}{space 2} .0632389{col 61}{space 1}   -0.31{col 70}{space 3}0.759{col 78}{space 4} -.143389{col 91}{space 3} .1045853
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} .1222299{col 50}{space 2} .0591923{col 61}{space 1}    2.06{col 70}{space 3}0.039{col 78}{space 4} .0061765{col 91}{space 3} .2382833
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 3.731615{col 50}{space 2} .1852556{col 61}{space 1}   20.14{col 70}{space 3}0.000{col 78}{space 4}   3.3684{col 91}{space 3} 4.094829
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. generate infullmodel_immig1 = e(sample)
{txt}
{com}. 
. 
. 
. *** Table E1: Support for Government Redistributing Incomes to the Less Well-Off ***
. 
. * A
. svy: reg polpreferences1_redistribution i.jobimpact2_AI if infullmodel_redist1 == 1
{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,633
{txt}{col 1}Number of PSUs{col 20}= {res}    3,633{txt}{col 49}Population size{col 67}={res}  3,737.245
{txt}{col 49}Design df{col 67}= {res}     3,632
{txt}{col 49}F({res}   3{txt},{res}   3630{txt}){col 67}= {res}      3.79
{txt}{col 49}Prob > F{col 67}= {res}    0.0100
{txt}{col 49}R-squared{col 67}= {res}    0.0037

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}  Linearized
{col 1}polpref~ution{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
jobimpact2_AI {c |}
{space 6}Worsen  {c |}{col 15}{res}{space 2} .1756838{col 27}{space 2} .0572368{col 38}{space 1}    3.07{col 47}{space 3}0.002{col 55}{space 4} .0634644{col 68}{space 3} .2879031
{txt}{space 5}Improve  {c |}{col 15}{res}{space 2} .1453862{col 27}{space 2} .0791198{col 38}{space 1}    1.84{col 47}{space 3}0.066{col 55}{space 4}-.0097374{col 68}{space 3} .3005099
{txt}{space 2}Don't Know  {c |}{col 15}{res}{space 2} .0173539{col 27}{space 2} .0668762{col 38}{space 1}    0.26{col 47}{space 3}0.795{col 55}{space 4}-.1137646{col 68}{space 3} .1484725
{txt}{space 13} {c |}
{space 8}_cons {c |}{col 15}{res}{space 2} 3.427641{col 27}{space 2}  .033681{col 38}{space 1}  101.77{col 47}{space 3}0.000{col 55}{space 4} 3.361606{col 68}{space 3} 3.493677
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. * B
. svy: reg polpreferences1_redistribution i.jobimpact2_AI $controls_demographics if infullmodel_redist1 == 1
{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,633
{txt}{col 1}Number of PSUs{col 20}= {res}    3,633{txt}{col 49}Population size{col 67}={res}  3,737.245
{txt}{col 49}Design df{col 67}= {res}     3,632
{txt}{col 49}F({res}  19{txt},{res}   3614{txt}){col 67}= {res}      6.54
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.0385

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}      polpreferences1_redistribution{col 38}{c |}      Coef.{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}jobimpact2_AI {c |}
{space 29}Worsen  {c |}{col 38}{res}{space 2} .1554141{col 50}{space 2} .0566658{col 61}{space 1}    2.74{col 70}{space 3}0.006{col 78}{space 4} .0443142{col 91}{space 3}  .266514
{txt}{space 28}Improve  {c |}{col 38}{res}{space 2} .0998389{col 50}{space 2} .0824994{col 61}{space 1}    1.21{col 70}{space 3}0.226{col 78}{space 4}-.0619108{col 91}{space 3} .2615887
{txt}{space 25}Don't Know  {c |}{col 38}{res}{space 2} .0009041{col 50}{space 2} .0663346{col 61}{space 1}    0.01{col 70}{space 3}0.989{col 78}{space 4}-.1291526{col 91}{space 3} .1309609
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2}-.0073743{col 50}{space 2} .0020283{col 61}{space 1}   -3.64{col 70}{space 3}0.000{col 78}{space 4}-.0113511{col 91}{space 3}-.0033975
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}-.0447814{col 50}{space 2} .0493404{col 61}{space 1}   -0.91{col 70}{space 3}0.364{col 78}{space 4}-.1415191{col 91}{space 3} .0519564
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .2473005{col 50}{space 2} .0507836{col 61}{space 1}    4.87{col 70}{space 3}0.000{col 78}{space 4} .1477333{col 91}{space 3} .3468677
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .0142951{col 50}{space 2} .0589947{col 61}{space 1}    0.24{col 70}{space 3}0.809{col 78}{space 4}-.1013709{col 91}{space 3} .1299612
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .0707824{col 50}{space 2} .0521791{col 61}{space 1}    1.36{col 70}{space 3}0.175{col 78}{space 4}-.0315209{col 91}{space 3} .1730857
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .3172364{col 50}{space 2} .1042276{col 61}{space 1}    3.04{col 70}{space 3}0.002{col 78}{space 4} .1128859{col 91}{space 3} .5215869
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2}-.1057071{col 50}{space 2} .0871779{col 61}{space 1}   -1.21{col 70}{space 3}0.225{col 78}{space 4}-.2766295{col 91}{space 3} .0652154
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .3038313{col 50}{space 2} .1335127{col 61}{space 1}    2.28{col 70}{space 3}0.023{col 78}{space 4}  .042064{col 91}{space 3} .5655985
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}-.0270987{col 50}{space 2} .0904478{col 61}{space 1}   -0.30{col 70}{space 3}0.764{col 78}{space 4}-.2044322{col 91}{space 3} .1502348
{txt}{space 34}3  {c |}{col 38}{res}{space 2}-.1187822{col 50}{space 2}  .091607{col 61}{space 1}   -1.30{col 70}{space 3}0.195{col 78}{space 4}-.2983884{col 91}{space 3} .0608239
{txt}{space 34}4  {c |}{col 38}{res}{space 2}-.3394353{col 50}{space 2} .0910528{col 61}{space 1}   -3.73{col 70}{space 3}0.000{col 78}{space 4} -.517955{col 91}{space 3}-.1609156
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2}-.5003034{col 50}{space 2} .0931189{col 61}{space 1}   -5.37{col 70}{space 3}0.000{col 78}{space 4}-.6828739{col 91}{space 3}-.3177328
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}-.0028236{col 50}{space 2} .0676894{col 61}{space 1}   -0.04{col 70}{space 3}0.967{col 78}{space 4}-.1355365{col 91}{space 3} .1298894
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}-.0889002{col 50}{space 2}  .074426{col 61}{space 1}   -1.19{col 70}{space 3}0.232{col 78}{space 4}-.2348212{col 91}{space 3} .0570207
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2}-.1146895{col 50}{space 2} .0751934{col 61}{space 1}   -1.53{col 70}{space 3}0.127{col 78}{space 4} -.262115{col 91}{space 3}  .032736
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2} .0824719{col 50}{space 2} .0828611{col 61}{space 1}    1.00{col 70}{space 3}0.320{col 78}{space 4} -.079987{col 91}{space 3} .2449308
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 3.917047{col 50}{space 2} .1414047{col 61}{space 1}   27.70{col 70}{space 3}0.000{col 78}{space 4} 3.639807{col 91}{space 3} 4.194288
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. * C
. svy: reg polpreferences1_redistribution i.jobimpact2_AI $controls_demographics $controls_politics if infullmodel_redist1 == 1
{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,633
{txt}{col 1}Number of PSUs{col 20}= {res}    3,633{txt}{col 49}Population size{col 67}={res}  3,737.245
{txt}{col 49}Design df{col 67}= {res}     3,632
{txt}{col 49}F({res}  33{txt},{res}   3600{txt}){col 67}= {res}     42.20
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.2576

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}      polpreferences1_redistribution{col 38}{c |}      Coef.{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}jobimpact2_AI {c |}
{space 29}Worsen  {c |}{col 38}{res}{space 2} .1085288{col 50}{space 2} .0505179{col 61}{space 1}    2.15{col 70}{space 3}0.032{col 78}{space 4} .0094825{col 91}{space 3} .2075752
{txt}{space 28}Improve  {c |}{col 38}{res}{space 2} .0845186{col 50}{space 2} .0733839{col 61}{space 1}    1.15{col 70}{space 3}0.250{col 78}{space 4}-.0593592{col 91}{space 3} .2283963
{txt}{space 25}Don't Know  {c |}{col 38}{res}{space 2} .0095878{col 50}{space 2} .0591472{col 61}{space 1}    0.16{col 70}{space 3}0.871{col 78}{space 4}-.1063772{col 91}{space 3} .1255528
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2} .0012853{col 50}{space 2} .0018249{col 61}{space 1}    0.70{col 70}{space 3}0.481{col 78}{space 4}-.0022926{col 91}{space 3} .0048631
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}-.1155048{col 50}{space 2} .0442295{col 61}{space 1}   -2.61{col 70}{space 3}0.009{col 78}{space 4} -.202222{col 91}{space 3}-.0287876
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .0406633{col 50}{space 2} .0456606{col 61}{space 1}    0.89{col 70}{space 3}0.373{col 78}{space 4}-.0488596{col 91}{space 3} .1301862
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2}-.0211233{col 50}{space 2}  .051799{col 61}{space 1}   -0.41{col 70}{space 3}0.683{col 78}{space 4}-.1226814{col 91}{space 3} .0804348
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .0023152{col 50}{space 2}  .047139{col 61}{space 1}    0.05{col 70}{space 3}0.961{col 78}{space 4}-.0901063{col 91}{space 3} .0947366
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}  .131318{col 50}{space 2} .0885901{col 61}{space 1}    1.48{col 70}{space 3}0.138{col 78}{space 4}-.0423734{col 91}{space 3} .3050093
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2}-.1515541{col 50}{space 2} .0735356{col 61}{space 1}   -2.06{col 70}{space 3}0.039{col 78}{space 4}-.2957292{col 91}{space 3}-.0073789
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .2263642{col 50}{space 2} .1190234{col 61}{space 1}    1.90{col 70}{space 3}0.057{col 78}{space 4}-.0069951{col 91}{space 3} .4597234
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}-.0350602{col 50}{space 2} .0807331{col 61}{space 1}   -0.43{col 70}{space 3}0.664{col 78}{space 4}-.1933469{col 91}{space 3} .1232265
{txt}{space 34}3  {c |}{col 38}{res}{space 2}-.0694019{col 50}{space 2} .0823381{col 61}{space 1}   -0.84{col 70}{space 3}0.399{col 78}{space 4}-.2308355{col 91}{space 3} .0920316
{txt}{space 34}4  {c |}{col 38}{res}{space 2}-.2862435{col 50}{space 2} .0815474{col 61}{space 1}   -3.51{col 70}{space 3}0.000{col 78}{space 4}-.4461267{col 91}{space 3}-.1263603
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} -.421039{col 50}{space 2} .0823766{col 61}{space 1}   -5.11{col 70}{space 3}0.000{col 78}{space 4}-.5825481{col 91}{space 3}-.2595299
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} .0636349{col 50}{space 2} .0595303{col 61}{space 1}    1.07{col 70}{space 3}0.285{col 78}{space 4}-.0530813{col 91}{space 3}  .180351
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}-.0826556{col 50}{space 2} .0652567{col 61}{space 1}   -1.27{col 70}{space 3}0.205{col 78}{space 4} -.210599{col 91}{space 3} .0452879
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2} -.017048{col 50}{space 2}  .067823{col 61}{space 1}   -0.25{col 70}{space 3}0.802{col 78}{space 4}-.1500229{col 91}{space 3} .1159268
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}  .125807{col 50}{space 2} .0762224{col 61}{space 1}    1.65{col 70}{space 3}0.099{col 78}{space 4}-.0236359{col 91}{space 3} .2752499
{txt}{space 36} {c |}
{space 29}pid2024 {c |}
{space 29}Labour  {c |}{col 38}{res}{space 2} .7395024{col 50}{space 2} .0817801{col 61}{space 1}    9.04{col 70}{space 3}0.000{col 78}{space 4} .5791628{col 91}{space 3}  .899842
{txt}{space 28}Liberal  {c |}{col 38}{res}{space 2} .7294435{col 50}{space 2} .1041522{col 61}{space 1}    7.00{col 70}{space 3}0.000{col 78}{space 4}  .525241{col 91}{space 3} .9336461
{txt}{space 30}Green  {c |}{col 38}{res}{space 2} .6973863{col 50}{space 2} .1068865{col 61}{space 1}    6.52{col 70}{space 3}0.000{col 78}{space 4} .4878228{col 91}{space 3} .9069499
{txt}{space 29}Reform  {c |}{col 38}{res}{space 2} .1637972{col 50}{space 2} .1323739{col 61}{space 1}    1.24{col 70}{space 3}0.216{col 78}{space 4}-.0957374{col 91}{space 3} .4233318
{txt}{space 30}Other  {c |}{col 38}{res}{space 2} .6637962{col 50}{space 2} .1179789{col 61}{space 1}    5.63{col 70}{space 3}0.000{col 78}{space 4} .4324848{col 91}{space 3} .8951076
{txt}{space 25}Don't Know  {c |}{col 38}{res}{space 2} .4352958{col 50}{space 2} .1048666{col 61}{space 1}    4.15{col 70}{space 3}0.000{col 78}{space 4} .2296926{col 91}{space 3}  .640899
{txt}{space 31}None  {c |}{col 38}{res}{space 2} .5495882{col 50}{space 2} .0782017{col 61}{space 1}    7.03{col 70}{space 3}0.000{col 78}{space 4} .3962645{col 91}{space 3} .7029119
{txt}{space 36} {c |}
{space 10}profile_leftrightplacement {c |}
{space 19}Fairly left-wing  {c |}{col 38}{res}{space 2}-.3183976{col 50}{space 2} .0846795{col 61}{space 1}   -3.76{col 70}{space 3}0.000{col 78}{space 4}-.4844217{col 91}{space 3}-.1523734
{txt}{space 12}Slightly left-of-centre  {c |}{col 38}{res}{space 2}-.7564685{col 50}{space 2} .0893009{col 61}{space 1}   -8.47{col 70}{space 3}0.000{col 78}{space 4}-.9315534{col 91}{space 3}-.5813836
{txt}{space 29}Centre  {c |}{col 38}{res}{space 2}-1.171209{col 50}{space 2} .0930982{col 61}{space 1}  -12.58{col 70}{space 3}0.000{col 78}{space 4}-1.353739{col 91}{space 3}-.9886787
{txt}{space 11}Slightly right-of-centre  {c |}{col 38}{res}{space 2}-1.621027{col 50}{space 2} .1090066{col 61}{space 1}  -14.87{col 70}{space 3}0.000{col 78}{space 4}-1.834748{col 91}{space 3}-1.407307
{txt}{space 18}Fairly right-wing  {c |}{col 38}{res}{space 2}-1.652885{col 50}{space 2} .1328576{col 61}{space 1}  -12.44{col 70}{space 3}0.000{col 78}{space 4}-1.913368{col 91}{space 3}-1.392402
{txt}{space 20}Very right-wing  {c |}{col 38}{res}{space 2}-1.754284{col 50}{space 2} .2495622{col 61}{space 1}   -7.03{col 70}{space 3}0.000{col 78}{space 4} -2.24358{col 91}{space 3}-1.264988
{txt}{space 25}Don’t know  {c |}{col 38}{res}{space 2}-1.205202{col 50}{space 2}    .0903{col 61}{space 1}  -13.35{col 70}{space 3}0.000{col 78}{space 4}-1.382245{col 91}{space 3}-1.028158
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 4.178628{col 50}{space 2} .1631859{col 61}{space 1}   25.61{col 70}{space 3}0.000{col 78}{space 4} 3.858683{col 91}{space 3} 4.498574
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. * D
. svy: reg polpreferences1_redistribution i.jobimpact2_AI $controls_objective $controls_demographics $controls_politics if infullmodel_redist1 == 1                      
{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,633
{txt}{col 1}Number of PSUs{col 20}= {res}    3,633{txt}{col 49}Population size{col 67}={res}  3,737.245
{txt}{col 49}Design df{col 67}= {res}     3,632
{txt}{col 49}F({res}  39{txt},{res}   3594{txt}){col 67}= {res}     36.62
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.2608

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}      polpreferences1_redistribution{col 38}{c |}      Coef.{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}jobimpact2_AI {c |}
{space 29}Worsen  {c |}{col 38}{res}{space 2} .1055692{col 50}{space 2} .0505023{col 61}{space 1}    2.09{col 70}{space 3}0.037{col 78}{space 4} .0065535{col 91}{space 3}  .204585
{txt}{space 28}Improve  {c |}{col 38}{res}{space 2} .0948219{col 50}{space 2} .0735572{col 61}{space 1}    1.29{col 70}{space 3}0.197{col 78}{space 4}-.0493956{col 91}{space 3} .2390393
{txt}{space 25}Don't Know  {c |}{col 38}{res}{space 2}  .013855{col 50}{space 2} .0590388{col 61}{space 1}    0.23{col 70}{space 3}0.814{col 78}{space 4}-.1018975{col 91}{space 3} .1296075
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2}-.0170566{col 50}{space 2} .0558349{col 61}{space 1}   -0.31{col 70}{space 3}0.760{col 78}{space 4}-.1265275{col 91}{space 3} .0924142
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2}-.1320424{col 50}{space 2} .1049473{col 61}{space 1}   -1.26{col 70}{space 3}0.208{col 78}{space 4}-.3378039{col 91}{space 3} .0737192
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2}-.2061814{col 50}{space 2} .1051801{col 61}{space 1}   -1.96{col 70}{space 3}0.050{col 78}{space 4}-.4123992{col 91}{space 3} .0000365
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .2111524{col 50}{space 2} .1232427{col 61}{space 1}    1.71{col 70}{space 3}0.087{col 78}{space 4}-.0304795{col 91}{space 3} .4527842
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2} .0027637{col 50}{space 2} .0658365{col 61}{space 1}    0.04{col 70}{space 3}0.967{col 78}{space 4}-.1263165{col 91}{space 3} .1318438
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} .1312173{col 50}{space 2} .0589496{col 61}{space 1}    2.23{col 70}{space 3}0.026{col 78}{space 4} .0156396{col 91}{space 3} .2467949
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2}  .001282{col 50}{space 2} .0018224{col 61}{space 1}    0.70{col 70}{space 3}0.482{col 78}{space 4}-.0022911{col 91}{space 3} .0048551
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}-.1103217{col 50}{space 2} .0442056{col 61}{space 1}   -2.50{col 70}{space 3}0.013{col 78}{space 4}-.1969921{col 91}{space 3}-.0236514
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .0462259{col 50}{space 2} .0457156{col 61}{space 1}    1.01{col 70}{space 3}0.312{col 78}{space 4}-.0434049{col 91}{space 3} .1358566
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} -.022575{col 50}{space 2} .0518905{col 61}{space 1}   -0.44{col 70}{space 3}0.664{col 78}{space 4}-.1243125{col 91}{space 3} .0791625
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .0258314{col 50}{space 2} .0479135{col 61}{space 1}    0.54{col 70}{space 3}0.590{col 78}{space 4}-.0681087{col 91}{space 3} .1197715
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .1531819{col 50}{space 2}  .089119{col 61}{space 1}    1.72{col 70}{space 3}0.086{col 78}{space 4}-.0215463{col 91}{space 3} .3279101
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2}-.1683704{col 50}{space 2} .0738443{col 61}{space 1}   -2.28{col 70}{space 3}0.023{col 78}{space 4}-.3131509{col 91}{space 3}-.0235899
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .2221254{col 50}{space 2} .1209937{col 61}{space 1}    1.84{col 70}{space 3}0.066{col 78}{space 4}-.0150969{col 91}{space 3} .4593477
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}-.0377159{col 50}{space 2} .0809142{col 61}{space 1}   -0.47{col 70}{space 3}0.641{col 78}{space 4}-.1963577{col 91}{space 3} .1209259
{txt}{space 34}3  {c |}{col 38}{res}{space 2} -.063061{col 50}{space 2} .0823252{col 61}{space 1}   -0.77{col 70}{space 3}0.444{col 78}{space 4}-.2244693{col 91}{space 3} .0983473
{txt}{space 34}4  {c |}{col 38}{res}{space 2}-.2810713{col 50}{space 2} .0815204{col 61}{space 1}   -3.45{col 70}{space 3}0.001{col 78}{space 4}-.4409015{col 91}{space 3} -.121241
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2}-.4132883{col 50}{space 2} .0827212{col 61}{space 1}   -5.00{col 70}{space 3}0.000{col 78}{space 4}-.5754729{col 91}{space 3}-.2511037
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}-.0155373{col 50}{space 2} .0627718{col 61}{space 1}   -0.25{col 70}{space 3}0.805{col 78}{space 4}-.1386088{col 91}{space 3} .1075342
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}-.2636036{col 50}{space 2} .1093962{col 61}{space 1}   -2.41{col 70}{space 3}0.016{col 78}{space 4}-.4780876{col 91}{space 3}-.0491196
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2}-.1081665{col 50}{space 2} .0776321{col 61}{space 1}   -1.39{col 70}{space 3}0.164{col 78}{space 4}-.2603732{col 91}{space 3} .0440403
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}-.0011309{col 50}{space 2} .0995273{col 61}{space 1}   -0.01{col 70}{space 3}0.991{col 78}{space 4}-.1962658{col 91}{space 3} .1940039
{txt}{space 36} {c |}
{space 29}pid2024 {c |}
{space 29}Labour  {c |}{col 38}{res}{space 2} .7377595{col 50}{space 2} .0817099{col 61}{space 1}    9.03{col 70}{space 3}0.000{col 78}{space 4} .5775577{col 91}{space 3} .8979613
{txt}{space 28}Liberal  {c |}{col 38}{res}{space 2} .7153253{col 50}{space 2} .1036983{col 61}{space 1}    6.90{col 70}{space 3}0.000{col 78}{space 4} .5120125{col 91}{space 3}  .918638
{txt}{space 30}Green  {c |}{col 38}{res}{space 2} .6940048{col 50}{space 2} .1058397{col 61}{space 1}    6.56{col 70}{space 3}0.000{col 78}{space 4} .4864936{col 91}{space 3}  .901516
{txt}{space 29}Reform  {c |}{col 38}{res}{space 2} .1602423{col 50}{space 2} .1324792{col 61}{space 1}    1.21{col 70}{space 3}0.227{col 78}{space 4}-.0994987{col 91}{space 3} .4199833
{txt}{space 30}Other  {c |}{col 38}{res}{space 2} .6586456{col 50}{space 2} .1186378{col 61}{space 1}    5.55{col 70}{space 3}0.000{col 78}{space 4} .4260422{col 91}{space 3}  .891249
{txt}{space 25}Don't Know  {c |}{col 38}{res}{space 2} .4363632{col 50}{space 2} .1049672{col 61}{space 1}    4.16{col 70}{space 3}0.000{col 78}{space 4} .2305627{col 91}{space 3} .6421637
{txt}{space 31}None  {c |}{col 38}{res}{space 2} .5491572{col 50}{space 2} .0777115{col 61}{space 1}    7.07{col 70}{space 3}0.000{col 78}{space 4} .3967946{col 91}{space 3} .7015197
{txt}{space 36} {c |}
{space 10}profile_leftrightplacement {c |}
{space 19}Fairly left-wing  {c |}{col 38}{res}{space 2}-.3134064{col 50}{space 2} .0850098{col 61}{space 1}   -3.69{col 70}{space 3}0.000{col 78}{space 4} -.480078{col 91}{space 3}-.1467347
{txt}{space 12}Slightly left-of-centre  {c |}{col 38}{res}{space 2}-.7476248{col 50}{space 2} .0893811{col 61}{space 1}   -8.36{col 70}{space 3}0.000{col 78}{space 4} -.922867{col 91}{space 3}-.5723826
{txt}{space 29}Centre  {c |}{col 38}{res}{space 2}-1.163259{col 50}{space 2} .0934701{col 61}{space 1}  -12.45{col 70}{space 3}0.000{col 78}{space 4}-1.346518{col 91}{space 3}-.9799998
{txt}{space 11}Slightly right-of-centre  {c |}{col 38}{res}{space 2}-1.616472{col 50}{space 2} .1092492{col 61}{space 1}  -14.80{col 70}{space 3}0.000{col 78}{space 4}-1.830668{col 91}{space 3}-1.402276
{txt}{space 18}Fairly right-wing  {c |}{col 38}{res}{space 2}-1.645728{col 50}{space 2} .1327517{col 61}{space 1}  -12.40{col 70}{space 3}0.000{col 78}{space 4}-1.906003{col 91}{space 3}-1.385453
{txt}{space 20}Very right-wing  {c |}{col 38}{res}{space 2}-1.742683{col 50}{space 2} .2496626{col 61}{space 1}   -6.98{col 70}{space 3}0.000{col 78}{space 4}-2.232176{col 91}{space 3} -1.25319
{txt}{space 25}Don’t know  {c |}{col 38}{res}{space 2}-1.197418{col 50}{space 2} .0908743{col 61}{space 1}  -13.18{col 70}{space 3}0.000{col 78}{space 4}-1.375587{col 91}{space 3}-1.019248
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 4.289795{col 50}{space 2} .1772194{col 61}{space 1}   24.21{col 70}{space 3}0.000{col 78}{space 4} 3.942336{col 91}{space 3} 4.637255
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. *** Table E2: Support for Government Spending on Free Adult Education / Re-Training ***
. 
. * A
. svy: reg polpreferences6_education i.jobimpact2_AI if infullmodel_education1 == 1
{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,537
{txt}{col 1}Number of PSUs{col 20}= {res}    3,537{txt}{col 49}Population size{col 67}={res} 3,649.7904
{txt}{col 49}Design df{col 67}= {res}     3,536
{txt}{col 49}F({res}   3{txt},{res}   3534{txt}){col 67}= {res}     15.67
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.0146

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}  Linearized
{col 1}polpre~cation{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
jobimpact2_AI {c |}
{space 6}Worsen  {c |}{col 15}{res}{space 2} .0718205{col 27}{space 2} .0549341{col 38}{space 1}    1.31{col 47}{space 3}0.191{col 55}{space 4}-.0358853{col 68}{space 3} .1795263
{txt}{space 5}Improve  {c |}{col 15}{res}{space 2} .4759713{col 27}{space 2} .0726737{col 38}{space 1}    6.55{col 47}{space 3}0.000{col 55}{space 4} .3334848{col 68}{space 3} .6184578
{txt}{space 2}Don't Know  {c |}{col 15}{res}{space 2}-.0325115{col 27}{space 2} .0605216{col 38}{space 1}   -0.54{col 47}{space 3}0.591{col 55}{space 4}-.1511722{col 68}{space 3} .0861492
{txt}{space 13} {c |}
{space 8}_cons {c |}{col 15}{res}{space 2} 2.592341{col 27}{space 2} .0310795{col 38}{space 1}   83.41{col 47}{space 3}0.000{col 55}{space 4} 2.531405{col 68}{space 3} 2.653276
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. * B
. svy: reg polpreferences6_education i.jobimpact2_AI $controls_demographics if infullmodel_education1 == 1
{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,537
{txt}{col 1}Number of PSUs{col 20}= {res}    3,537{txt}{col 49}Population size{col 67}={res} 3,649.7904
{txt}{col 49}Design df{col 67}= {res}     3,536
{txt}{col 49}F({res}  19{txt},{res}   3518{txt}){col 67}= {res}      9.87
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.0603

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}           polpreferences6_education{col 38}{c |}      Coef.{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}jobimpact2_AI {c |}
{space 29}Worsen  {c |}{col 38}{res}{space 2} .0065405{col 50}{space 2} .0543108{col 61}{space 1}    0.12{col 70}{space 3}0.904{col 78}{space 4}-.0999432{col 91}{space 3} .1130241
{txt}{space 28}Improve  {c |}{col 38}{res}{space 2} .3698351{col 50}{space 2} .0747106{col 61}{space 1}    4.95{col 70}{space 3}0.000{col 78}{space 4} .2233549{col 91}{space 3} .5163154
{txt}{space 25}Don't Know  {c |}{col 38}{res}{space 2}-.0322194{col 50}{space 2} .0600502{col 61}{space 1}   -0.54{col 70}{space 3}0.592{col 78}{space 4}-.1499559{col 91}{space 3}  .085517
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2}-.0042678{col 50}{space 2} .0019207{col 61}{space 1}   -2.22{col 70}{space 3}0.026{col 78}{space 4}-.0080337{col 91}{space 3} -.000502
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}-.0584846{col 50}{space 2} .0453953{col 61}{space 1}   -1.29{col 70}{space 3}0.198{col 78}{space 4}-.1474883{col 91}{space 3}  .030519
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2}  .341719{col 50}{space 2} .0473089{col 61}{space 1}    7.22{col 70}{space 3}0.000{col 78}{space 4} .2489636{col 91}{space 3} .4344744
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .1897231{col 50}{space 2} .0543074{col 61}{space 1}    3.49{col 70}{space 3}0.000{col 78}{space 4} .0832461{col 91}{space 3} .2962002
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .1120052{col 50}{space 2} .0488856{col 61}{space 1}    2.29{col 70}{space 3}0.022{col 78}{space 4} .0161583{col 91}{space 3} .2078521
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .1331663{col 50}{space 2} .0999389{col 61}{space 1}    1.33{col 70}{space 3}0.183{col 78}{space 4}-.0627774{col 91}{space 3}   .32911
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .1880341{col 50}{space 2} .0754821{col 61}{space 1}    2.49{col 70}{space 3}0.013{col 78}{space 4} .0400412{col 91}{space 3} .3360269
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .3507741{col 50}{space 2} .1586386{col 61}{space 1}    2.21{col 70}{space 3}0.027{col 78}{space 4} .0397417{col 91}{space 3} .6618065
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .0692691{col 50}{space 2} .0850198{col 61}{space 1}    0.81{col 70}{space 3}0.415{col 78}{space 4}-.0974236{col 91}{space 3} .2359619
{txt}{space 34}3  {c |}{col 38}{res}{space 2}-.0547108{col 50}{space 2} .0838635{col 61}{space 1}   -0.65{col 70}{space 3}0.514{col 78}{space 4}-.2191365{col 91}{space 3} .1097149
{txt}{space 34}4  {c |}{col 38}{res}{space 2}-.1523939{col 50}{space 2} .0808135{col 61}{space 1}   -1.89{col 70}{space 3}0.059{col 78}{space 4}-.3108396{col 91}{space 3} .0060518
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2}-.1514525{col 50}{space 2} .0836865{col 61}{space 1}   -1.81{col 70}{space 3}0.070{col 78}{space 4}-.3155311{col 91}{space 3} .0126261
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}-.0611865{col 50}{space 2} .0609289{col 61}{space 1}   -1.00{col 70}{space 3}0.315{col 78}{space 4}-.1806458{col 91}{space 3} .0582728
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}-.0828185{col 50}{space 2} .0681139{col 61}{space 1}   -1.22{col 70}{space 3}0.224{col 78}{space 4}-.2163649{col 91}{space 3} .0507279
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2}-.1457365{col 50}{space 2} .0698729{col 61}{space 1}   -2.09{col 70}{space 3}0.037{col 78}{space 4}-.2827318{col 91}{space 3}-.0087411
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}-.3082782{col 50}{space 2}   .07718{col 61}{space 1}   -3.99{col 70}{space 3}0.000{col 78}{space 4}-.4596001{col 91}{space 3}-.1569564
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 2.738216{col 50}{space 2} .1307158{col 61}{space 1}   20.95{col 70}{space 3}0.000{col 78}{space 4}  2.48193{col 91}{space 3} 2.994502
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. * C
. svy: reg polpreferences6_education i.jobimpact2_AI $controls_demographics $controls_politics if infullmodel_education1 == 1
{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,537
{txt}{col 1}Number of PSUs{col 20}= {res}    3,537{txt}{col 49}Population size{col 67}={res} 3,649.7904
{txt}{col 49}Design df{col 67}= {res}     3,536
{txt}{col 49}F({res}  33{txt},{res}   3504{txt}){col 67}= {res}     25.01
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.2004

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}           polpreferences6_education{col 38}{c |}      Coef.{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}jobimpact2_AI {c |}
{space 29}Worsen  {c |}{col 38}{res}{space 2}-.0284254{col 50}{space 2} .0502711{col 61}{space 1}   -0.57{col 70}{space 3}0.572{col 78}{space 4}-.1269886{col 91}{space 3} .0701379
{txt}{space 28}Improve  {c |}{col 38}{res}{space 2} .3449934{col 50}{space 2} .0680336{col 61}{space 1}    5.07{col 70}{space 3}0.000{col 78}{space 4} .2116043{col 91}{space 3} .4783826
{txt}{space 25}Don't Know  {c |}{col 38}{res}{space 2}-.0040731{col 50}{space 2} .0568048{col 61}{space 1}   -0.07{col 70}{space 3}0.943{col 78}{space 4}-.1154467{col 91}{space 3} .1073005
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2} .0002476{col 50}{space 2} .0018307{col 61}{space 1}    0.14{col 70}{space 3}0.892{col 78}{space 4}-.0033418{col 91}{space 3}  .003837
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}-.0993162{col 50}{space 2} .0422128{col 61}{space 1}   -2.35{col 70}{space 3}0.019{col 78}{space 4}  -.18208{col 91}{space 3}-.0165524
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .1626611{col 50}{space 2} .0443781{col 61}{space 1}    3.67{col 70}{space 3}0.000{col 78}{space 4} .0756518{col 91}{space 3} .2496703
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .1579841{col 50}{space 2} .0513803{col 61}{space 1}    3.07{col 70}{space 3}0.002{col 78}{space 4} .0572461{col 91}{space 3} .2587221
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2}  .076311{col 50}{space 2} .0461136{col 61}{space 1}    1.65{col 70}{space 3}0.098{col 78}{space 4} -.014101{col 91}{space 3}  .166723
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}-.0064701{col 50}{space 2} .0827584{col 61}{space 1}   -0.08{col 70}{space 3}0.938{col 78}{space 4}-.1687291{col 91}{space 3} .1557889
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .1428703{col 50}{space 2} .0667419{col 61}{space 1}    2.14{col 70}{space 3}0.032{col 78}{space 4} .0120137{col 91}{space 3} .2737269
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .3616095{col 50}{space 2} .1594264{col 61}{space 1}    2.27{col 70}{space 3}0.023{col 78}{space 4} .0490325{col 91}{space 3} .6741865
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2}  .069422{col 50}{space 2} .0812269{col 61}{space 1}    0.85{col 70}{space 3}0.393{col 78}{space 4}-.0898343{col 91}{space 3} .2286783
{txt}{space 34}3  {c |}{col 38}{res}{space 2}-.0022161{col 50}{space 2} .0800471{col 61}{space 1}   -0.03{col 70}{space 3}0.978{col 78}{space 4}-.1591593{col 91}{space 3}  .154727
{txt}{space 34}4  {c |}{col 38}{res}{space 2}-.1001508{col 50}{space 2} .0773056{col 61}{space 1}   -1.30{col 70}{space 3}0.195{col 78}{space 4}-.2517189{col 91}{space 3} .0514173
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2}-.1003142{col 50}{space 2} .0805571{col 61}{space 1}   -1.25{col 70}{space 3}0.213{col 78}{space 4}-.2582573{col 91}{space 3} .0576289
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}-.0234891{col 50}{space 2} .0549237{col 61}{space 1}   -0.43{col 70}{space 3}0.669{col 78}{space 4}-.1311745{col 91}{space 3} .0841963
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}-.0647754{col 50}{space 2} .0624872{col 61}{space 1}   -1.04{col 70}{space 3}0.300{col 78}{space 4}-.1872899{col 91}{space 3} .0577391
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2}-.0564068{col 50}{space 2}  .066009{col 61}{space 1}   -0.85{col 70}{space 3}0.393{col 78}{space 4}-.1858264{col 91}{space 3} .0730128
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}-.2097636{col 50}{space 2} .0719981{col 61}{space 1}   -2.91{col 70}{space 3}0.004{col 78}{space 4}-.3509256{col 91}{space 3}-.0686016
{txt}{space 36} {c |}
{space 29}pid2024 {c |}
{space 29}Labour  {c |}{col 38}{res}{space 2} .2594711{col 50}{space 2} .0758205{col 61}{space 1}    3.42{col 70}{space 3}0.001{col 78}{space 4} .1108148{col 91}{space 3} .4081274
{txt}{space 28}Liberal  {c |}{col 38}{res}{space 2} .3162915{col 50}{space 2} .1003679{col 61}{space 1}    3.15{col 70}{space 3}0.002{col 78}{space 4} .1195068{col 91}{space 3} .5130763
{txt}{space 30}Green  {c |}{col 38}{res}{space 2} .3660071{col 50}{space 2} .1084659{col 61}{space 1}    3.37{col 70}{space 3}0.001{col 78}{space 4} .1533451{col 91}{space 3} .5786692
{txt}{space 29}Reform  {c |}{col 38}{res}{space 2}-.2143931{col 50}{space 2} .1003239{col 61}{space 1}   -2.14{col 70}{space 3}0.033{col 78}{space 4}-.4110917{col 91}{space 3}-.0176945
{txt}{space 30}Other  {c |}{col 38}{res}{space 2}  .428444{col 50}{space 2}   .13501{col 61}{space 1}    3.17{col 70}{space 3}0.002{col 78}{space 4} .1637386{col 91}{space 3} .6931493
{txt}{space 25}Don't Know  {c |}{col 38}{res}{space 2} .2789461{col 50}{space 2} .0939554{col 61}{space 1}    2.97{col 70}{space 3}0.003{col 78}{space 4} .0947338{col 91}{space 3} .4631584
{txt}{space 31}None  {c |}{col 38}{res}{space 2} .0273444{col 50}{space 2} .0673365{col 61}{space 1}    0.41{col 70}{space 3}0.685{col 78}{space 4} -.104678{col 91}{space 3} .1593667
{txt}{space 36} {c |}
{space 10}profile_leftrightplacement {c |}
{space 19}Fairly left-wing  {c |}{col 38}{res}{space 2}-.3420118{col 50}{space 2} .1207741{col 61}{space 1}   -2.83{col 70}{space 3}0.005{col 78}{space 4}-.5788058{col 91}{space 3}-.1052179
{txt}{space 12}Slightly left-of-centre  {c |}{col 38}{res}{space 2}-.7329032{col 50}{space 2} .1240847{col 61}{space 1}   -5.91{col 70}{space 3}0.000{col 78}{space 4}-.9761879{col 91}{space 3}-.4896184
{txt}{space 29}Centre  {c |}{col 38}{res}{space 2}-1.001366{col 50}{space 2} .1251659{col 61}{space 1}   -8.00{col 70}{space 3}0.000{col 78}{space 4}-1.246771{col 91}{space 3}-.7559615
{txt}{space 11}Slightly right-of-centre  {c |}{col 38}{res}{space 2}-1.267924{col 50}{space 2} .1330965{col 61}{space 1}   -9.53{col 70}{space 3}0.000{col 78}{space 4}-1.528878{col 91}{space 3} -1.00697
{txt}{space 18}Fairly right-wing  {c |}{col 38}{res}{space 2}-1.425322{col 50}{space 2} .1437462{col 61}{space 1}   -9.92{col 70}{space 3}0.000{col 78}{space 4}-1.707156{col 91}{space 3}-1.143488
{txt}{space 20}Very right-wing  {c |}{col 38}{res}{space 2}-1.536396{col 50}{space 2} .2035401{col 61}{space 1}   -7.55{col 70}{space 3}0.000{col 78}{space 4}-1.935463{col 91}{space 3}-1.137328
{txt}{space 25}Don’t know  {c |}{col 38}{res}{space 2}-1.192015{col 50}{space 2} .1238815{col 61}{space 1}   -9.62{col 70}{space 3}0.000{col 78}{space 4}-1.434901{col 91}{space 3}-.9491282
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 3.419735{col 50}{space 2} .1785705{col 61}{space 1}   19.15{col 70}{space 3}0.000{col 78}{space 4} 3.069623{col 91}{space 3} 3.769846
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. * D
. svy: reg polpreferences6_education i.jobimpact2_AI $controls_objective $controls_demographics $controls_politics if infullmodel_education1 == 1
{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,537
{txt}{col 1}Number of PSUs{col 20}= {res}    3,537{txt}{col 49}Population size{col 67}={res} 3,649.7904
{txt}{col 49}Design df{col 67}= {res}     3,536
{txt}{col 49}F({res}  39{txt},{res}   3498{txt}){col 67}= {res}     21.80
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.2027

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}           polpreferences6_education{col 38}{c |}      Coef.{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}jobimpact2_AI {c |}
{space 29}Worsen  {c |}{col 38}{res}{space 2}-.0351956{col 50}{space 2} .0502827{col 61}{space 1}   -0.70{col 70}{space 3}0.484{col 78}{space 4}-.1337816{col 91}{space 3} .0633904
{txt}{space 28}Improve  {c |}{col 38}{res}{space 2} .3349367{col 50}{space 2} .0681122{col 61}{space 1}    4.92{col 70}{space 3}0.000{col 78}{space 4} .2013934{col 91}{space 3} .4684799
{txt}{space 25}Don't Know  {c |}{col 38}{res}{space 2}-.0059064{col 50}{space 2} .0569648{col 61}{space 1}   -0.10{col 70}{space 3}0.917{col 78}{space 4}-.1175936{col 91}{space 3} .1057808
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2}  .035949{col 50}{space 2}  .052046{col 61}{space 1}    0.69{col 70}{space 3}0.490{col 78}{space 4}-.0660943{col 91}{space 3} .1379922
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2} .0699452{col 50}{space 2} .1028301{col 61}{space 1}    0.68{col 70}{space 3}0.496{col 78}{space 4}-.1316671{col 91}{space 3} .2715576
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2} .0203655{col 50}{space 2} .1041149{col 61}{space 1}    0.20{col 70}{space 3}0.845{col 78}{space 4}-.1837658{col 91}{space 3} .2244968
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2}-.1996798{col 50}{space 2} .1187045{col 61}{space 1}   -1.68{col 70}{space 3}0.093{col 78}{space 4}-.4324159{col 91}{space 3} .0330564
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2}-.0918086{col 50}{space 2} .0642117{col 61}{space 1}   -1.43{col 70}{space 3}0.153{col 78}{space 4}-.2177043{col 91}{space 3} .0340871
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2}-.0070072{col 50}{space 2} .0589003{col 61}{space 1}   -0.12{col 70}{space 3}0.905{col 78}{space 4}-.1224891{col 91}{space 3} .1084747
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2} .0004198{col 50}{space 2} .0018274{col 61}{space 1}    0.23{col 70}{space 3}0.818{col 78}{space 4}-.0031632{col 91}{space 3} .0040028
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}-.0933917{col 50}{space 2} .0422919{col 61}{space 1}   -2.21{col 70}{space 3}0.027{col 78}{space 4}-.1763107{col 91}{space 3}-.0104727
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .1645408{col 50}{space 2} .0443555{col 61}{space 1}    3.71{col 70}{space 3}0.000{col 78}{space 4} .0775758{col 91}{space 3} .2515058
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .1584539{col 50}{space 2} .0515919{col 61}{space 1}    3.07{col 70}{space 3}0.002{col 78}{space 4} .0573011{col 91}{space 3} .2596068
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2}  .079805{col 50}{space 2} .0472987{col 61}{space 1}    1.69{col 70}{space 3}0.092{col 78}{space 4}-.0129304{col 91}{space 3} .1725405
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2}-.0011325{col 50}{space 2} .0832354{col 61}{space 1}   -0.01{col 70}{space 3}0.989{col 78}{space 4}-.1643268{col 91}{space 3} .1620618
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .1524948{col 50}{space 2} .0670412{col 61}{space 1}    2.27{col 70}{space 3}0.023{col 78}{space 4} .0210515{col 91}{space 3} .2839381
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2}  .362627{col 50}{space 2} .1586247{col 61}{space 1}    2.29{col 70}{space 3}0.022{col 78}{space 4} .0516218{col 91}{space 3} .6736322
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .0740994{col 50}{space 2} .0809831{col 61}{space 1}    0.91{col 70}{space 3}0.360{col 78}{space 4}-.0846789{col 91}{space 3} .2328778
{txt}{space 34}3  {c |}{col 38}{res}{space 2}-.0011869{col 50}{space 2} .0794739{col 61}{space 1}   -0.01{col 70}{space 3}0.988{col 78}{space 4}-.1570061{col 91}{space 3} .1546324
{txt}{space 34}4  {c |}{col 38}{res}{space 2}-.1004544{col 50}{space 2} .0767597{col 61}{space 1}   -1.31{col 70}{space 3}0.191{col 78}{space 4} -.250952{col 91}{space 3} .0500433
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2}-.1059311{col 50}{space 2} .0799563{col 61}{space 1}   -1.32{col 70}{space 3}0.185{col 78}{space 4}-.2626961{col 91}{space 3}  .050834
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}-.0367639{col 50}{space 2} .0607841{col 61}{space 1}   -0.60{col 70}{space 3}0.545{col 78}{space 4}-.1559393{col 91}{space 3} .0824114
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2} .0356075{col 50}{space 2} .1044856{col 61}{space 1}    0.34{col 70}{space 3}0.733{col 78}{space 4}-.1692507{col 91}{space 3} .2404658
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2}-.0088752{col 50}{space 2} .0806831{col 61}{space 1}   -0.11{col 70}{space 3}0.912{col 78}{space 4}-.1670652{col 91}{space 3} .1493149
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}-.1260558{col 50}{space 2} .0995952{col 61}{space 1}   -1.27{col 70}{space 3}0.206{col 78}{space 4}-.3213256{col 91}{space 3}  .069214
{txt}{space 36} {c |}
{space 29}pid2024 {c |}
{space 29}Labour  {c |}{col 38}{res}{space 2}  .262007{col 50}{space 2} .0759241{col 61}{space 1}    3.45{col 70}{space 3}0.001{col 78}{space 4} .1131476{col 91}{space 3} .4108664
{txt}{space 28}Liberal  {c |}{col 38}{res}{space 2} .3194845{col 50}{space 2} .1000014{col 61}{space 1}    3.19{col 70}{space 3}0.001{col 78}{space 4} .1234183{col 91}{space 3} .5155506
{txt}{space 30}Green  {c |}{col 38}{res}{space 2} .3723776{col 50}{space 2} .1078641{col 61}{space 1}    3.45{col 70}{space 3}0.001{col 78}{space 4} .1608954{col 91}{space 3} .5838598
{txt}{space 29}Reform  {c |}{col 38}{res}{space 2}-.2166618{col 50}{space 2} .1001926{col 61}{space 1}   -2.16{col 70}{space 3}0.031{col 78}{space 4}-.4131028{col 91}{space 3}-.0202207
{txt}{space 30}Other  {c |}{col 38}{res}{space 2} .4230259{col 50}{space 2} .1350682{col 61}{space 1}    3.13{col 70}{space 3}0.002{col 78}{space 4} .1582066{col 91}{space 3} .6878453
{txt}{space 25}Don't Know  {c |}{col 38}{res}{space 2} .2794445{col 50}{space 2} .0938785{col 61}{space 1}    2.98{col 70}{space 3}0.003{col 78}{space 4}  .095383{col 91}{space 3} .4635061
{txt}{space 31}None  {c |}{col 38}{res}{space 2} .0308751{col 50}{space 2} .0672775{col 61}{space 1}    0.46{col 70}{space 3}0.646{col 78}{space 4}-.1010316{col 91}{space 3} .1627818
{txt}{space 36} {c |}
{space 10}profile_leftrightplacement {c |}
{space 19}Fairly left-wing  {c |}{col 38}{res}{space 2}-.3307315{col 50}{space 2} .1189601{col 61}{space 1}   -2.78{col 70}{space 3}0.005{col 78}{space 4}-.5639688{col 91}{space 3}-.0974941
{txt}{space 12}Slightly left-of-centre  {c |}{col 38}{res}{space 2}-.7206547{col 50}{space 2} .1221966{col 61}{space 1}   -5.90{col 70}{space 3}0.000{col 78}{space 4}-.9602377{col 91}{space 3}-.4810716
{txt}{space 29}Centre  {c |}{col 38}{res}{space 2} -.989026{col 50}{space 2} .1233284{col 61}{space 1}   -8.02{col 70}{space 3}0.000{col 78}{space 4}-1.230828{col 91}{space 3}-.7472241
{txt}{space 11}Slightly right-of-centre  {c |}{col 38}{res}{space 2}-1.249679{col 50}{space 2} .1314978{col 61}{space 1}   -9.50{col 70}{space 3}0.000{col 78}{space 4}-1.507498{col 91}{space 3}-.9918598
{txt}{space 18}Fairly right-wing  {c |}{col 38}{res}{space 2}-1.405782{col 50}{space 2} .1423871{col 61}{space 1}   -9.87{col 70}{space 3}0.000{col 78}{space 4}-1.684951{col 91}{space 3}-1.126613
{txt}{space 20}Very right-wing  {c |}{col 38}{res}{space 2}-1.518217{col 50}{space 2} .2034459{col 61}{space 1}   -7.46{col 70}{space 3}0.000{col 78}{space 4}-1.917101{col 91}{space 3}-1.119334
{txt}{space 25}Don’t know  {c |}{col 38}{res}{space 2}-1.177068{col 50}{space 2} .1222226{col 61}{space 1}   -9.63{col 70}{space 3}0.000{col 78}{space 4}-1.416702{col 91}{space 3}-.9374343
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2}  3.34574{col 50}{space 2} .1882306{col 61}{space 1}   17.77{col 70}{space 3}0.000{col 78}{space 4} 2.976689{col 91}{space 3} 3.714792
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. *** Table E3: Support for Government Liberalising Britain's Immigration Controls ***
. 
. * A
. svy: reg polpreferences4_immigration i.jobimpact2_AI if infullmodel_immig1 == 1
{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,645
{txt}{col 1}Number of PSUs{col 20}= {res}    3,645{txt}{col 49}Population size{col 67}={res} 3,761.5796
{txt}{col 49}Design df{col 67}= {res}     3,644
{txt}{col 49}F({res}   3{txt},{res}   3642{txt}){col 67}= {res}     15.15
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.0147

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}  Linearized
{col 1}polpre~ration{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
jobimpact2_AI {c |}
{space 6}Worsen  {c |}{col 15}{res}{space 2}-.0442714{col 27}{space 2} .0600519{col 38}{space 1}   -0.74{col 47}{space 3}0.461{col 55}{space 4}-.1620101{col 68}{space 3} .0734672
{txt}{space 5}Improve  {c |}{col 15}{res}{space 2} .4705618{col 27}{space 2} .0821174{col 38}{space 1}    5.73{col 47}{space 3}0.000{col 55}{space 4} .3095611{col 68}{space 3} .6315624
{txt}{space 2}Don't Know  {c |}{col 15}{res}{space 2}-.1385206{col 27}{space 2} .0657347{col 38}{space 1}   -2.11{col 47}{space 3}0.035{col 55}{space 4}-.2674011{col 68}{space 3}-.0096401
{txt}{space 13} {c |}
{space 8}_cons {c |}{col 15}{res}{space 2} 2.651457{col 27}{space 2} .0328028{col 38}{space 1}   80.83{col 47}{space 3}0.000{col 55}{space 4} 2.587143{col 68}{space 3} 2.715771
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. * B
. svy: reg polpreferences4_immigration i.jobimpact2_AI $controls_demographics if infullmodel_immig1 == 1
{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,645
{txt}{col 1}Number of PSUs{col 20}= {res}    3,645{txt}{col 49}Population size{col 67}={res} 3,761.5796
{txt}{col 49}Design df{col 67}= {res}     3,644
{txt}{col 49}F({res}  19{txt},{res}   3626{txt}){col 67}= {res}     31.13
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1444

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}         polpreferences4_immigration{col 38}{c |}      Coef.{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}jobimpact2_AI {c |}
{space 29}Worsen  {c |}{col 38}{res}{space 2}-.1716588{col 50}{space 2} .0564443{col 61}{space 1}   -3.04{col 70}{space 3}0.002{col 78}{space 4}-.2823243{col 91}{space 3}-.0609932
{txt}{space 28}Improve  {c |}{col 38}{res}{space 2} .1865203{col 50}{space 2} .0797565{col 61}{space 1}    2.34{col 70}{space 3}0.019{col 78}{space 4} .0301485{col 91}{space 3} .3428922
{txt}{space 25}Don't Know  {c |}{col 38}{res}{space 2}-.1221588{col 50}{space 2} .0616631{col 61}{space 1}   -1.98{col 70}{space 3}0.048{col 78}{space 4}-.2430564{col 91}{space 3}-.0012613
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2}-.0184386{col 50}{space 2} .0019206{col 61}{space 1}   -9.60{col 70}{space 3}0.000{col 78}{space 4}-.0222041{col 91}{space 3} -.014673
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .1080442{col 50}{space 2} .0470013{col 61}{space 1}    2.30{col 70}{space 3}0.022{col 78}{space 4} .0158928{col 91}{space 3} .2001956
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .5382307{col 50}{space 2} .0494324{col 61}{space 1}   10.89{col 70}{space 3}0.000{col 78}{space 4} .4413129{col 91}{space 3} .6351486
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2}  .168206{col 50}{space 2} .0563127{col 61}{space 1}    2.99{col 70}{space 3}0.003{col 78}{space 4} .0577984{col 91}{space 3} .2786135
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .0702357{col 50}{space 2} .0502383{col 61}{space 1}    1.40{col 70}{space 3}0.162{col 78}{space 4}-.0282622{col 91}{space 3} .1687336
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .3054046{col 50}{space 2} .1054546{col 61}{space 1}    2.90{col 70}{space 3}0.004{col 78}{space 4} .0986486{col 91}{space 3} .5121605
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .1618121{col 50}{space 2}  .081902{col 61}{space 1}    1.98{col 70}{space 3}0.048{col 78}{space 4} .0012337{col 91}{space 3} .3223905
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .6171552{col 50}{space 2} .1550276{col 61}{space 1}    3.98{col 70}{space 3}0.000{col 78}{space 4} .3132056{col 91}{space 3} .9211048
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .1539581{col 50}{space 2} .0866444{col 61}{space 1}    1.78{col 70}{space 3}0.076{col 78}{space 4}-.0159182{col 91}{space 3} .3238343
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .0867977{col 50}{space 2} .0843403{col 61}{space 1}    1.03{col 70}{space 3}0.303{col 78}{space 4}-.0785612{col 91}{space 3} .2521566
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .1262001{col 50}{space 2} .0813083{col 61}{space 1}    1.55{col 70}{space 3}0.121{col 78}{space 4}-.0332143{col 91}{space 3} .2856144
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .2656242{col 50}{space 2} .0872526{col 61}{space 1}    3.04{col 70}{space 3}0.002{col 78}{space 4} .0945554{col 91}{space 3}  .436693
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2} -.135864{col 50}{space 2} .0656913{col 61}{space 1}   -2.07{col 70}{space 3}0.039{col 78}{space 4}-.2646592{col 91}{space 3}-.0070687
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}-.2174278{col 50}{space 2} .0690236{col 61}{space 1}   -3.15{col 70}{space 3}0.002{col 78}{space 4}-.3527566{col 91}{space 3} -.082099
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2}-.2716747{col 50}{space 2} .0721936{col 61}{space 1}   -3.76{col 70}{space 3}0.000{col 78}{space 4}-.4132185{col 91}{space 3}-.1301309
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}-.3463803{col 50}{space 2}  .083312{col 61}{space 1}   -4.16{col 70}{space 3}0.000{col 78}{space 4} -.509723{col 91}{space 3}-.1830376
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2}  3.11197{col 50}{space 2} .1276202{col 61}{space 1}   24.38{col 70}{space 3}0.000{col 78}{space 4} 2.861756{col 91}{space 3} 3.362184
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. * C
. svy: reg polpreferences4_immigration i.jobimpact2_AI $controls_demographics $controls_politics if infullmodel_immig1 == 1
{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,645
{txt}{col 1}Number of PSUs{col 20}= {res}    3,645{txt}{col 49}Population size{col 67}={res} 3,761.5796
{txt}{col 49}Design df{col 67}= {res}     3,644
{txt}{col 49}F({res}  33{txt},{res}   3612{txt}){col 67}= {res}     66.22
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.3275

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}         polpreferences4_immigration{col 38}{c |}      Coef.{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}jobimpact2_AI {c |}
{space 29}Worsen  {c |}{col 38}{res}{space 2}-.2007219{col 50}{space 2}  .049945{col 61}{space 1}   -4.02{col 70}{space 3}0.000{col 78}{space 4}-.2986449{col 91}{space 3}-.1027989
{txt}{space 28}Improve  {c |}{col 38}{res}{space 2} .1630015{col 50}{space 2}  .076784{col 61}{space 1}    2.12{col 70}{space 3}0.034{col 78}{space 4} .0124576{col 91}{space 3} .3135453
{txt}{space 25}Don't Know  {c |}{col 38}{res}{space 2}-.1064347{col 50}{space 2} .0565635{col 61}{space 1}   -1.88{col 70}{space 3}0.060{col 78}{space 4}-.2173341{col 91}{space 3} .0044646
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2}-.0119817{col 50}{space 2} .0017585{col 61}{space 1}   -6.81{col 70}{space 3}0.000{col 78}{space 4}-.0154295{col 91}{space 3}-.0085339
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .0418549{col 50}{space 2} .0430332{col 61}{space 1}    0.97{col 70}{space 3}0.331{col 78}{space 4}-.0425166{col 91}{space 3} .1262264
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .3188514{col 50}{space 2} .0451427{col 61}{space 1}    7.06{col 70}{space 3}0.000{col 78}{space 4}  .230344{col 91}{space 3} .4073588
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .1348686{col 50}{space 2} .0509983{col 61}{space 1}    2.64{col 70}{space 3}0.008{col 78}{space 4} .0348805{col 91}{space 3} .2348566
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .0255227{col 50}{space 2} .0456281{col 61}{space 1}    0.56{col 70}{space 3}0.576{col 78}{space 4}-.0639364{col 91}{space 3} .1149819
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .1512505{col 50}{space 2}  .085101{col 61}{space 1}    1.78{col 70}{space 3}0.076{col 78}{space 4}-.0155998{col 91}{space 3} .3181009
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .1117485{col 50}{space 2} .0769853{col 61}{space 1}    1.45{col 70}{space 3}0.147{col 78}{space 4}-.0391901{col 91}{space 3} .2626871
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .6067487{col 50}{space 2} .1537277{col 61}{space 1}    3.95{col 70}{space 3}0.000{col 78}{space 4} .3053479{col 91}{space 3} .9081496
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .1520266{col 50}{space 2} .0831544{col 61}{space 1}    1.83{col 70}{space 3}0.068{col 78}{space 4}-.0110073{col 91}{space 3} .3150604
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .1358218{col 50}{space 2} .0778128{col 61}{space 1}    1.75{col 70}{space 3}0.081{col 78}{space 4}-.0167392{col 91}{space 3} .2883828
{txt}{space 34}4  {c |}{col 38}{res}{space 2} .1811231{col 50}{space 2}  .075285{col 61}{space 1}    2.41{col 70}{space 3}0.016{col 78}{space 4} .0335183{col 91}{space 3}  .328728
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .3227995{col 50}{space 2} .0821943{col 61}{space 1}    3.93{col 70}{space 3}0.000{col 78}{space 4} .1616481{col 91}{space 3} .4839508
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}-.0809434{col 50}{space 2} .0580518{col 61}{space 1}   -1.39{col 70}{space 3}0.163{col 78}{space 4}-.1947606{col 91}{space 3} .0328739
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}-.1779839{col 50}{space 2} .0592272{col 61}{space 1}   -3.01{col 70}{space 3}0.003{col 78}{space 4}-.2941057{col 91}{space 3}-.0618621
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2}-.1867839{col 50}{space 2} .0689346{col 61}{space 1}   -2.71{col 70}{space 3}0.007{col 78}{space 4}-.3219382{col 91}{space 3}-.0516297
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}-.2455865{col 50}{space 2} .0769848{col 61}{space 1}   -3.19{col 70}{space 3}0.001{col 78}{space 4}-.3965241{col 91}{space 3}-.0946489
{txt}{space 36} {c |}
{space 29}pid2024 {c |}
{space 29}Labour  {c |}{col 38}{res}{space 2}  .374193{col 50}{space 2} .0782088{col 61}{space 1}    4.78{col 70}{space 3}0.000{col 78}{space 4} .2208557{col 91}{space 3} .5275303
{txt}{space 28}Liberal  {c |}{col 38}{res}{space 2} .6073885{col 50}{space 2} .1055609{col 61}{space 1}    5.75{col 70}{space 3}0.000{col 78}{space 4} .4004241{col 91}{space 3} .8143529
{txt}{space 30}Green  {c |}{col 38}{res}{space 2} .6459906{col 50}{space 2} .1058176{col 61}{space 1}    6.10{col 70}{space 3}0.000{col 78}{space 4}  .438523{col 91}{space 3} .8534582
{txt}{space 29}Reform  {c |}{col 38}{res}{space 2}-.5064021{col 50}{space 2} .0835183{col 61}{space 1}   -6.06{col 70}{space 3}0.000{col 78}{space 4}-.6701495{col 91}{space 3}-.3426548
{txt}{space 30}Other  {c |}{col 38}{res}{space 2} .5288426{col 50}{space 2} .1320818{col 61}{space 1}    4.00{col 70}{space 3}0.000{col 78}{space 4} .2698811{col 91}{space 3} .7878041
{txt}{space 25}Don't Know  {c |}{col 38}{res}{space 2} .3201427{col 50}{space 2} .0964267{col 61}{space 1}    3.32{col 70}{space 3}0.001{col 78}{space 4} .1310869{col 91}{space 3} .5091984
{txt}{space 31}None  {c |}{col 38}{res}{space 2} .1764136{col 50}{space 2} .0700463{col 61}{space 1}    2.52{col 70}{space 3}0.012{col 78}{space 4} .0390797{col 91}{space 3} .3137474
{txt}{space 36} {c |}
{space 10}profile_leftrightplacement {c |}
{space 19}Fairly left-wing  {c |}{col 38}{res}{space 2}-.4537689{col 50}{space 2} .1111322{col 61}{space 1}   -4.08{col 70}{space 3}0.000{col 78}{space 4}-.6716563{col 91}{space 3}-.2358814
{txt}{space 12}Slightly left-of-centre  {c |}{col 38}{res}{space 2}-.7925364{col 50}{space 2}  .114689{col 61}{space 1}   -6.91{col 70}{space 3}0.000{col 78}{space 4}-1.017397{col 91}{space 3}-.5676753
{txt}{space 29}Centre  {c |}{col 38}{res}{space 2}-1.131059{col 50}{space 2}  .118533{col 61}{space 1}   -9.54{col 70}{space 3}0.000{col 78}{space 4}-1.363456{col 91}{space 3}-.8986612
{txt}{space 11}Slightly right-of-centre  {c |}{col 38}{res}{space 2}-1.475272{col 50}{space 2} .1259901{col 61}{space 1}  -11.71{col 70}{space 3}0.000{col 78}{space 4} -1.72229{col 91}{space 3}-1.228254
{txt}{space 18}Fairly right-wing  {c |}{col 38}{res}{space 2}-1.690786{col 50}{space 2} .1380846{col 61}{space 1}  -12.24{col 70}{space 3}0.000{col 78}{space 4}-1.961517{col 91}{space 3}-1.420055
{txt}{space 20}Very right-wing  {c |}{col 38}{res}{space 2}-1.979958{col 50}{space 2} .1716174{col 61}{space 1}  -11.54{col 70}{space 3}0.000{col 78}{space 4}-2.316433{col 91}{space 3}-1.643482
{txt}{space 25}Don’t know  {c |}{col 38}{res}{space 2}-1.316875{col 50}{space 2} .1161027{col 61}{space 1}  -11.34{col 70}{space 3}0.000{col 78}{space 4}-1.544507{col 91}{space 3}-1.089242
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 3.766825{col 50}{space 2} .1700343{col 61}{space 1}   22.15{col 70}{space 3}0.000{col 78}{space 4} 3.433453{col 91}{space 3} 4.100196
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. * D
. svy: reg polpreferences4_immigration i.jobimpact2_AI $controls_objective $controls_demographics $controls_politics if infullmodel_immig1 == 1
{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}     3,645
{txt}{col 1}Number of PSUs{col 20}= {res}    3,645{txt}{col 49}Population size{col 67}={res} 3,761.5796
{txt}{col 49}Design df{col 67}= {res}     3,644
{txt}{col 49}F({res}  39{txt},{res}   3606{txt}){col 67}= {res}     57.39
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.3297

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}  Linearized
{col 1}         polpreferences4_immigration{col 38}{c |}      Coef.{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}jobimpact2_AI {c |}
{space 29}Worsen  {c |}{col 38}{res}{space 2} -.208027{col 50}{space 2} .0493213{col 61}{space 1}   -4.22{col 70}{space 3}0.000{col 78}{space 4}-.3047271{col 91}{space 3} -.111327
{txt}{space 28}Improve  {c |}{col 38}{res}{space 2} .1552427{col 50}{space 2} .0766498{col 61}{space 1}    2.03{col 70}{space 3}0.043{col 78}{space 4}  .004962{col 91}{space 3} .3055235
{txt}{space 25}Don't Know  {c |}{col 38}{res}{space 2}-.1085006{col 50}{space 2}  .056494{col 61}{space 1}   -1.92{col 70}{space 3}0.055{col 78}{space 4}-.2192635{col 91}{space 3} .0022623
{txt}{space 36} {c |}
{space 15}aiexposure_felten2021 {c |}{col 38}{res}{space 2} .0561296{col 50}{space 2}  .054422{col 61}{space 1}    1.03{col 70}{space 3}0.302{col 78}{space 4}-.0505709{col 91}{space 3} .1628302
{txt}{space 36} {c |}
{space 11}aiexposure_pizzinelli2023 {c |}
{space 6}High Exposure, Low Compliment  {c |}{col 38}{res}{space 2}-.0734838{col 50}{space 2} .1105749{col 61}{space 1}   -0.66{col 70}{space 3}0.506{col 78}{space 4}-.2902787{col 91}{space 3} .1433111
{txt}{space 5}High Exposure, High Compliment  {c |}{col 38}{res}{space 2}-.0792279{col 50}{space 2} .1084728{col 61}{space 1}   -0.73{col 70}{space 3}0.465{col 78}{space 4}-.2919013{col 91}{space 3} .1334455
{txt}{space 36} {c |}
{space 15}aiexposure_gmyrek2023 {c |}
{space 15}Automation Potential  {c |}{col 38}{res}{space 2} .0105041{col 50}{space 2} .1066825{col 61}{space 1}    0.10{col 70}{space 3}0.922{col 78}{space 4}-.1986592{col 91}{space 3} .2196673
{txt}{space 13}Augmentation Potential  {c |}{col 38}{res}{space 2}-.0194018{col 50}{space 2} .0632389{col 61}{space 1}   -0.31{col 70}{space 3}0.759{col 78}{space 4} -.143389{col 91}{space 3} .1045853
{txt}{space 24}Big Unknown  {c |}{col 38}{res}{space 2} .1222299{col 50}{space 2} .0591923{col 61}{space 1}    2.06{col 70}{space 3}0.039{col 78}{space 4} .0061765{col 91}{space 3} .2382833
{txt}{space 36} {c |}
{space 33}age {c |}{col 38}{res}{space 2}-.0119631{col 50}{space 2} .0017527{col 61}{space 1}   -6.83{col 70}{space 3}0.000{col 78}{space 4}-.0153994{col 91}{space 3}-.0085267
{txt}{space 36} {c |}
{space 24}female_dummy {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .0422359{col 50}{space 2} .0432439{col 61}{space 1}    0.98{col 70}{space 3}0.329{col 78}{space 4}-.0425488{col 91}{space 3} .1270207
{txt}{space 36} {c |}
{space 24}degree_dummy {c |}
{space 22}Degree-Holder  {c |}{col 38}{res}{space 2} .3189511{col 50}{space 2}  .045408{col 61}{space 1}    7.02{col 70}{space 3}0.000{col 78}{space 4} .2299235{col 91}{space 3} .4079787
{txt}{space 36} {c |}
{space 19}employmentstatus5 {c |}
{space 26}PT Worker  {c |}{col 38}{res}{space 2} .1355655{col 50}{space 2}  .051204{col 61}{space 1}    2.65{col 70}{space 3}0.008{col 78}{space 4} .0351742{col 91}{space 3} .2359568
{txt}{space 36} {c |}
{space 23}employsector5 {c |}
{space 13}Public Sector Employee  {c |}{col 38}{res}{space 2} .0370549{col 50}{space 2} .0464658{col 61}{space 1}    0.80{col 70}{space 3}0.425{col 78}{space 4}-.0540466{col 91}{space 3} .1281564
{txt}Charity / Voluntary Sector Employee  {c |}{col 38}{res}{space 2} .1634418{col 50}{space 2} .0849533{col 61}{space 1}    1.92{col 70}{space 3}0.054{col 78}{space 4}-.0031189{col 91}{space 3} .3300025
{txt}{space 6}Self-Employed / Company Owner  {c |}{col 38}{res}{space 2} .1145333{col 50}{space 2} .0778316{col 61}{space 1}    1.47{col 70}{space 3}0.141{col 78}{space 4}-.0380645{col 91}{space 3}  .267131
{txt}{space 25}Other / DK  {c |}{col 38}{res}{space 2} .6025441{col 50}{space 2}  .152622{col 61}{space 1}    3.95{col 70}{space 3}0.000{col 78}{space 4} .3033112{col 91}{space 3} .9017771
{txt}{space 36} {c |}
{space 6}equivincomeHHquintile_HBAI_imp {c |}
{space 34}2  {c |}{col 38}{res}{space 2} .1515783{col 50}{space 2} .0833631{col 61}{space 1}    1.82{col 70}{space 3}0.069{col 78}{space 4}-.0118646{col 91}{space 3} .3150212
{txt}{space 34}3  {c |}{col 38}{res}{space 2} .1385061{col 50}{space 2} .0779738{col 61}{space 1}    1.78{col 70}{space 3}0.076{col 78}{space 4}-.0143706{col 91}{space 3} .2913828
{txt}{space 34}4  {c |}{col 38}{res}{space 2}  .181332{col 50}{space 2}  .075445{col 61}{space 1}    2.40{col 70}{space 3}0.016{col 78}{space 4} .0334133{col 91}{space 3} .3292506
{txt}{space 23}Top Quintile  {c |}{col 38}{res}{space 2} .3170084{col 50}{space 2} .0821159{col 61}{space 1}    3.86{col 70}{space 3}0.000{col 78}{space 4} .1560107{col 91}{space 3} .4780062
{txt}{space 36} {c |}
{space 23}isco08_5group {c |}
{space 2}Technician/Associate Professional  {c |}{col 38}{res}{space 2}-.1170635{col 50}{space 2} .0617218{col 61}{space 1}   -1.90{col 70}{space 3}0.058{col 78}{space 4}-.2380762{col 91}{space 3} .0039492
{txt}{space 11}Clerical Support Workers  {c |}{col 38}{res}{space 2}-.1795786{col 50}{space 2} .0925368{col 61}{space 1}   -1.94{col 70}{space 3}0.052{col 78}{space 4}-.3610076{col 91}{space 3} .0018504
{txt}{space 10}Service and Sales Workers  {c |}{col 38}{res}{space 2}-.1540218{col 50}{space 2} .0830966{col 61}{space 1}   -1.85{col 70}{space 3}0.064{col 78}{space 4}-.3169424{col 91}{space 3} .0088987
{txt}{space 15}Manual Working Class  {c |}{col 38}{res}{space 2}-.1671382{col 50}{space 2} .1089614{col 61}{space 1}   -1.53{col 70}{space 3}0.125{col 78}{space 4}-.3807696{col 91}{space 3} .0464932
{txt}{space 36} {c |}
{space 29}pid2024 {c |}
{space 29}Labour  {c |}{col 38}{res}{space 2} .3773833{col 50}{space 2} .0782941{col 61}{space 1}    4.82{col 70}{space 3}0.000{col 78}{space 4} .2238786{col 91}{space 3} .5308879
{txt}{space 28}Liberal  {c |}{col 38}{res}{space 2} .6032731{col 50}{space 2} .1061544{col 61}{space 1}    5.68{col 70}{space 3}0.000{col 78}{space 4} .3951451{col 91}{space 3} .8114011
{txt}{space 30}Green  {c |}{col 38}{res}{space 2}  .654981{col 50}{space 2} .1056069{col 61}{space 1}    6.20{col 70}{space 3}0.000{col 78}{space 4} .4479266{col 91}{space 3} .8620354
{txt}{space 29}Reform  {c |}{col 38}{res}{space 2} -.508061{col 50}{space 2} .0830595{col 61}{space 1}   -6.12{col 70}{space 3}0.000{col 78}{space 4}-.6709088{col 91}{space 3}-.3452133
{txt}{space 30}Other  {c |}{col 38}{res}{space 2} .5267196{col 50}{space 2} .1319776{col 61}{space 1}    3.99{col 70}{space 3}0.000{col 78}{space 4} .2679624{col 91}{space 3} .7854769
{txt}{space 25}Don't Know  {c |}{col 38}{res}{space 2} .3241368{col 50}{space 2} .0963942{col 61}{space 1}    3.36{col 70}{space 3}0.001{col 78}{space 4} .1351448{col 91}{space 3} .5131288
{txt}{space 31}None  {c |}{col 38}{res}{space 2} .1821247{col 50}{space 2} .0701614{col 61}{space 1}    2.60{col 70}{space 3}0.009{col 78}{space 4} .0445652{col 91}{space 3} .3196843
{txt}{space 36} {c |}
{space 10}profile_leftrightplacement {c |}
{space 19}Fairly left-wing  {c |}{col 38}{res}{space 2}-.4442855{col 50}{space 2} .1106493{col 61}{space 1}   -4.02{col 70}{space 3}0.000{col 78}{space 4}-.6612263{col 91}{space 3}-.2273447
{txt}{space 12}Slightly left-of-centre  {c |}{col 38}{res}{space 2}-.7824472{col 50}{space 2} .1143844{col 61}{space 1}   -6.84{col 70}{space 3}0.000{col 78}{space 4}-1.006711{col 91}{space 3}-.5581833
{txt}{space 29}Centre  {c |}{col 38}{res}{space 2}-1.118947{col 50}{space 2} .1182519{col 61}{space 1}   -9.46{col 70}{space 3}0.000{col 78}{space 4}-1.350793{col 91}{space 3}-.8871006
{txt}{space 11}Slightly right-of-centre  {c |}{col 38}{res}{space 2}-1.459984{col 50}{space 2} .1257269{col 61}{space 1}  -11.61{col 70}{space 3}0.000{col 78}{space 4}-1.706486{col 91}{space 3}-1.213482
{txt}{space 18}Fairly right-wing  {c |}{col 38}{res}{space 2}-1.678568{col 50}{space 2} .1376944{col 61}{space 1}  -12.19{col 70}{space 3}0.000{col 78}{space 4}-1.948534{col 91}{space 3}-1.408603
{txt}{space 20}Very right-wing  {c |}{col 38}{res}{space 2}-1.969072{col 50}{space 2} .1715861{col 61}{space 1}  -11.48{col 70}{space 3}0.000{col 78}{space 4}-2.305487{col 91}{space 3}-1.632658
{txt}{space 25}Don’t know  {c |}{col 38}{res}{space 2}-1.306213{col 50}{space 2}  .115668{col 61}{space 1}  -11.29{col 70}{space 3}0.000{col 78}{space 4}-1.532994{col 91}{space 3}-1.079433
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 3.731615{col 50}{space 2} .1852556{col 61}{space 1}   20.14{col 70}{space 3}0.000{col 78}{space 4}   3.3684{col 91}{space 3} 4.094829
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
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{txt}end of do-file

{com}. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\Zack.Grant\Nuffield College Dropbox\Zack Grant\ECONOMIC INSECURITY\AI Risks Paper\Journal Submission\Research and Politics\Replication\AIpaperreplication_log.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}18 Nov 2024, 14:46:10
{txt}{.-}
{smcl}
{txt}{sf}{ul off}